torsh_fx/
cloud_deployment.rs

1//! Cloud Deployment Tools and Integrations
2//!
3//! This module provides utilities for deploying FX graphs to various cloud platforms
4//! including AWS, Google Cloud Platform (GCP), and Microsoft Azure. It handles:
5//! - Model packaging for cloud deployment
6//! - Container image generation (Docker)
7//! - Cloud-specific configuration files
8//! - Serverless deployment configurations
9//! - Auto-scaling and load balancing setup
10//! - Monitoring and logging integration
11
12use crate::model_zoo::{ModelMetadata, ModelZooEntry};
13use serde::{Deserialize, Serialize};
14use std::collections::HashMap;
15use std::fs;
16use std::path::{Path, PathBuf};
17use torsh_core::error::{Result, TorshError};
18
19/// Graph executor emitted into the generated inference server.
20///
21/// The exported `model.json` carries the serialized FX graph, so the server
22/// executes that graph with numpy instead of guessing an output. Operations whose
23/// parameters are not part of the export (linear, conv2d, batch_norm, ...) raise
24/// `NotImplementedError`, which the prediction endpoint turns into HTTP 501 — a
25/// generated service must never substitute an identity for the model.
26const GRAPH_EXECUTOR_PY: &str = r#"
27def _predecessors(node_id):
28    """Return the ids feeding a node, in edge declaration order."""
29    return [src for src, dst in GRAPH_EDGES if dst == node_id]
30
31
32def _topological_order():
33    """Return graph node ids in dependency order."""
34    indegree = {node_id: 0 for node_id in GRAPH_NODES}
35    for _, dst in GRAPH_EDGES:
36        if dst in indegree:
37            indegree[dst] += 1
38    ready = [node_id for node_id, degree in indegree.items() if degree == 0]
39    order = []
40    while ready:
41        current = ready.pop()
42        order.append(current)
43        for src, dst in GRAPH_EDGES:
44            if src == current and dst in indegree:
45                indegree[dst] -= 1
46                if indegree[dst] == 0:
47                    ready.append(dst)
48    if len(order) != len(GRAPH_NODES):
49        raise ValueError('exported graph contains a cycle and cannot be executed')
50    return order
51
52
53def _apply_op(op_name, operands):
54    """Evaluate a single graph operation with numpy."""
55    if op_name in ('relu', 'relu_inplace'):
56        return np.maximum(operands[0], 0.0)
57    if op_name in ('sigmoid', 'sigmoid_inplace'):
58        return 1.0 / (1.0 + np.exp(-operands[0]))
59    if op_name in ('tanh', 'tanh_inplace'):
60        return np.tanh(operands[0])
61    if op_name == 'gelu':
62        x = operands[0]
63        inner = np.sqrt(2.0 / np.pi) * (x + 0.044715 * np.power(x, 3))
64        return 0.5 * x * (1.0 + np.tanh(inner))
65    if op_name == 'softmax':
66        x = operands[0]
67        shifted = x - np.max(x, axis=-1, keepdims=True)
68        exps = np.exp(shifted)
69        return exps / np.sum(exps, axis=-1, keepdims=True)
70    if op_name == 'exp':
71        return np.exp(operands[0])
72    if op_name == 'log':
73        return np.log(operands[0])
74    if op_name == 'sqrt':
75        return np.sqrt(operands[0])
76    if op_name == 'neg':
77        return -operands[0]
78    if op_name == 'abs':
79        return np.abs(operands[0])
80    if op_name in ('add', 'add_inplace'):
81        return operands[0] + operands[1]
82    if op_name == 'sub':
83        return operands[0] - operands[1]
84    if op_name in ('mul', 'mul_inplace'):
85        return operands[0] * operands[1]
86    if op_name == 'div':
87        return operands[0] / operands[1]
88    if op_name == 'matmul':
89        return np.matmul(operands[0], operands[1])
90    if op_name == 'identity':
91        return operands[0]
92    if op_name == 'constant_zero':
93        return np.zeros(1, dtype=np.float32)
94    if op_name == 'constant_one':
95        return np.ones(1, dtype=np.float32)
96    raise NotImplementedError(
97        "operation '" + op_name + "' cannot be executed from the exported graph: it "
98        "requires model parameters that are not part of model.json. Supply a runtime "
99        "that provides the weights for this operation, or export a graph that only "
100        "uses parameter-free operations."
101    )
102
103
104def run_graph(input_arrays):
105    """Execute the exported graph and return its outputs as nested lists."""
106    values = {}
107    graph_inputs = GRAPH['inputs']
108    if len(input_arrays) != len(graph_inputs):
109        raise ValueError(
110            'expected %d input(s), received %d' % (len(graph_inputs), len(input_arrays))
111        )
112    for node_id, array in zip(graph_inputs, input_arrays):
113        values[node_id] = array
114
115    for node_id in _topological_order():
116        if node_id in values:
117            continue
118        node = GRAPH_NODES.get(node_id)
119        if node is None:
120            raise ValueError('graph references unknown node %r' % (node_id,))
121        node_type = node['node_type']
122        if node_type.startswith('input:'):
123            raise ValueError('no value supplied for graph input %r' % (node_id,))
124        preds = _predecessors(node_id)
125        if node_type == 'output':
126            if preds:
127                values[node_id] = values[preds[0]]
128            continue
129        if node_type.startswith('call:'):
130            op_name = node_type.split(':', 1)[1]
131            if op_name == 'constant':
132                params = node.get('params', {})
133                values[node_id] = np.asarray(
134                    [float(params.get('value', 0.0))], dtype=np.float32
135                )
136                continue
137            values[node_id] = _apply_op(op_name, [values[p] for p in preds])
138            continue
139        raise NotImplementedError(
140            "graph node type '" + node_type + "' is not supported by the generated server"
141        )
142
143    return [
144        np.asarray(values[node_id]).tolist()
145        for node_id in GRAPH['outputs']
146        if node_id in values
147    ]
148
149
150"#;
151
152/// Prediction endpoint emitted into the generated inference server.
153///
154/// Delegates to [`GRAPH_EXECUTOR_PY`]'s `run_graph` and maps a refusal to HTTP 501
155/// so an unrunnable model is visible instead of silently echoing the request.
156const PREDICT_ENDPOINT_PY: &str = r#"@app.route('/predict', methods=['POST'])
157def predict():
158    global request_count, total_inference_time, error_count
159    try:
160        start_time = time.time()
161
162        data = request.get_json()
163        if not data or 'inputs' not in data:
164            return jsonify({'error': 'Missing inputs'}), 400
165
166        inputs = data.get('inputs')
167
168        # Convert the request payload into the graph's input arrays
169        if len(GRAPH['inputs']) == 1:
170            input_arrays = [np.array(inputs, dtype=np.float32)]
171        else:
172            input_arrays = [np.array(item, dtype=np.float32) for item in inputs]
173
174        # Run the exported graph
175        outputs = run_graph(input_arrays)
176
177        inference_time = time.time() - start_time
178        request_count += 1
179        total_inference_time += inference_time
180
181        return jsonify({
182            'outputs': outputs,
183            'inference_time_ms': inference_time * 1000,
184            'request_id': request_count
185        })
186    except NotImplementedError as exc:
187        error_count += 1
188        logger.error(f'Model cannot be executed: {exc}')
189        return jsonify({'error': str(exc)}), 501
190    except Exception as e:
191        error_count += 1
192        logger.error(f'Prediction error: {e}')
193        return jsonify({'error': str(e)}), 500
194
195"#;
196
197/// Cloud deployment target
198#[derive(Debug, Clone, Serialize, Deserialize)]
199pub enum CloudPlatform {
200    /// Amazon Web Services
201    AWS { region: String, service: AWSService },
202    /// Google Cloud Platform
203    GCP {
204        project_id: String,
205        region: String,
206        service: GCPService,
207    },
208    /// Microsoft Azure
209    Azure {
210        subscription_id: String,
211        resource_group: String,
212        region: String,
213        service: AzureService,
214    },
215    /// Custom cloud platform
216    Custom { name: String, endpoint: String },
217}
218
219/// AWS service options
220#[derive(Debug, Clone, Serialize, Deserialize)]
221pub enum AWSService {
222    /// Amazon SageMaker
223    SageMaker {
224        instance_type: String,
225        endpoint_name: String,
226    },
227    /// AWS Lambda (serverless)
228    Lambda {
229        runtime: String,
230        memory_mb: usize,
231        timeout_seconds: usize,
232    },
233    /// Amazon ECS (container service)
234    ECS {
235        cluster_name: String,
236        task_definition: String,
237    },
238    /// Amazon EKS (Kubernetes)
239    EKS {
240        cluster_name: String,
241        namespace: String,
242    },
243}
244
245/// GCP service options
246#[derive(Debug, Clone, Serialize, Deserialize)]
247pub enum GCPService {
248    /// Vertex AI
249    VertexAI {
250        model_name: String,
251        machine_type: String,
252    },
253    /// Cloud Run (serverless)
254    CloudRun {
255        service_name: String,
256        memory_mb: usize,
257        max_instances: usize,
258    },
259    /// Google Kubernetes Engine
260    GKE {
261        cluster_name: String,
262        namespace: String,
263    },
264    /// Cloud Functions
265    CloudFunctions {
266        function_name: String,
267        runtime: String,
268        memory_mb: usize,
269    },
270}
271
272/// Azure service options
273#[derive(Debug, Clone, Serialize, Deserialize)]
274pub enum AzureService {
275    /// Azure Machine Learning
276    AzureML {
277        workspace_name: String,
278        endpoint_name: String,
279    },
280    /// Azure Functions (serverless)
281    AzureFunctions {
282        function_app_name: String,
283        runtime: String,
284    },
285    /// Azure Kubernetes Service
286    AKS {
287        cluster_name: String,
288        namespace: String,
289    },
290    /// Azure Container Instances
291    ACI {
292        container_group_name: String,
293        cpu_cores: f32,
294        memory_gb: f32,
295    },
296}
297
298/// Deployment configuration
299#[derive(Debug, Clone, Serialize, Deserialize)]
300pub struct DeploymentConfig {
301    /// Deployment name
302    pub name: String,
303    /// Cloud platform
304    pub platform: CloudPlatform,
305    /// Container configuration
306    pub container: ContainerConfig,
307    /// Resource requirements
308    pub resources: ResourceRequirements,
309    /// Auto-scaling configuration
310    pub autoscaling: Option<AutoScalingConfig>,
311    /// Environment variables
312    pub environment_variables: HashMap<String, String>,
313    /// Health check configuration
314    pub health_check: HealthCheckConfig,
315    /// Monitoring and logging
316    pub monitoring: MonitoringConfig,
317}
318
319/// Container configuration
320#[derive(Debug, Clone, Serialize, Deserialize)]
321pub struct ContainerConfig {
322    /// Base Docker image
323    pub base_image: String,
324    /// Python version
325    pub python_version: String,
326    /// Additional system packages
327    pub system_packages: Vec<String>,
328    /// Python packages
329    pub python_packages: Vec<String>,
330    /// Entry point command
331    pub entrypoint: Vec<String>,
332    /// Port to expose
333    pub port: u16,
334}
335
336/// Resource requirements
337#[derive(Debug, Clone, Serialize, Deserialize)]
338pub struct ResourceRequirements {
339    /// CPU cores
340    pub cpu_cores: f32,
341    /// Memory in GB
342    pub memory_gb: f32,
343    /// GPU count
344    pub gpu_count: u32,
345    /// GPU type
346    pub gpu_type: Option<String>,
347    /// Storage in GB
348    pub storage_gb: u32,
349}
350
351/// Auto-scaling configuration
352#[derive(Debug, Clone, Serialize, Deserialize)]
353pub struct AutoScalingConfig {
354    /// Minimum instances
355    pub min_instances: u32,
356    /// Maximum instances
357    pub max_instances: u32,
358    /// Target CPU utilization (percentage)
359    pub target_cpu_utilization: f32,
360    /// Target memory utilization (percentage)
361    pub target_memory_utilization: f32,
362    /// Scale-up cooldown (seconds)
363    pub scale_up_cooldown: u32,
364    /// Scale-down cooldown (seconds)
365    pub scale_down_cooldown: u32,
366}
367
368/// Health check configuration
369#[derive(Debug, Clone, Serialize, Deserialize)]
370pub struct HealthCheckConfig {
371    /// Health check path
372    pub path: String,
373    /// Check interval (seconds)
374    pub interval_seconds: u32,
375    /// Timeout (seconds)
376    pub timeout_seconds: u32,
377    /// Healthy threshold
378    pub healthy_threshold: u32,
379    /// Unhealthy threshold
380    pub unhealthy_threshold: u32,
381}
382
383/// Monitoring configuration
384#[derive(Debug, Clone, Serialize, Deserialize)]
385pub struct MonitoringConfig {
386    /// Enable metrics collection
387    pub enable_metrics: bool,
388    /// Enable logging
389    pub enable_logging: bool,
390    /// Enable distributed tracing
391    pub enable_tracing: bool,
392    /// Metrics endpoint
393    pub metrics_endpoint: Option<String>,
394    /// Log level
395    pub log_level: String,
396    /// Custom metrics
397    pub custom_metrics: Vec<String>,
398}
399
400/// Cloud deployment packager
401pub struct CloudDeploymentPackager {
402    /// Output directory
403    output_dir: PathBuf,
404    /// Deployment configuration
405    config: DeploymentConfig,
406}
407
408impl CloudDeploymentPackager {
409    /// Create a new deployment packager
410    pub fn new<P: AsRef<Path>>(output_dir: P, config: DeploymentConfig) -> Result<Self> {
411        let output_dir = output_dir.as_ref().to_path_buf();
412
413        // Create output directory
414        fs::create_dir_all(&output_dir).map_err(|e| TorshError::IoError(e.to_string()))?;
415
416        Ok(Self { output_dir, config })
417    }
418
419    /// Package model for deployment
420    pub fn package_model(&self, entry: &ModelZooEntry) -> Result<DeploymentPackage> {
421        // Create deployment structure
422        let deployment_dir = self.output_dir.join(&self.config.name);
423        fs::create_dir_all(&deployment_dir).map_err(|e| TorshError::IoError(e.to_string()))?;
424
425        // Generate Dockerfile
426        let dockerfile = self.generate_dockerfile(entry)?;
427        fs::write(deployment_dir.join("Dockerfile"), dockerfile)
428            .map_err(|e| TorshError::IoError(e.to_string()))?;
429
430        // Generate inference server
431        let server_code = self.generate_inference_server(entry)?;
432        fs::write(deployment_dir.join("server.py"), server_code)
433            .map_err(|e| TorshError::IoError(e.to_string()))?;
434
435        // Generate requirements.txt
436        let requirements = self.generate_requirements()?;
437        fs::write(deployment_dir.join("requirements.txt"), requirements)
438            .map_err(|e| TorshError::IoError(e.to_string()))?;
439
440        // Generate platform-specific configuration
441        let platform_config = self.generate_platform_config(entry)?;
442        fs::write(
443            deployment_dir.join("platform_config.json"),
444            serde_json::to_string_pretty(&platform_config)
445                .map_err(|e| TorshError::SerializationError(e.to_string()))?,
446        )
447        .map_err(|e| TorshError::IoError(e.to_string()))?;
448
449        // Generate deployment scripts
450        let deploy_script = self.generate_deployment_script()?;
451        fs::write(deployment_dir.join("deploy.sh"), deploy_script)
452            .map_err(|e| TorshError::IoError(e.to_string()))?;
453
454        // Copy model files
455        entry.save_to_file(deployment_dir.join("model.json"))?;
456
457        Ok(DeploymentPackage {
458            path: deployment_dir,
459            config: self.config.clone(),
460            metadata: entry.metadata.clone(),
461        })
462    }
463
464    /// Generate Dockerfile
465    fn generate_dockerfile(&self, _entry: &ModelZooEntry) -> Result<String> {
466        let mut dockerfile = String::new();
467
468        dockerfile.push_str(&format!("FROM {}\n\n", self.config.container.base_image));
469
470        dockerfile.push_str("WORKDIR /app\n\n");
471
472        // Install system packages
473        if !self.config.container.system_packages.is_empty() {
474            dockerfile.push_str("RUN apt-get update && apt-get install -y \\\n");
475            for pkg in &self.config.container.system_packages {
476                dockerfile.push_str(&format!("    {} \\\n", pkg));
477            }
478            dockerfile.push_str("    && rm -rf /var/lib/apt/lists/*\n\n");
479        }
480
481        // Copy files
482        dockerfile.push_str("COPY requirements.txt .\n");
483        dockerfile.push_str("RUN pip install --no-cache-dir -r requirements.txt\n\n");
484        dockerfile.push_str("COPY . .\n\n");
485
486        // Expose port
487        dockerfile.push_str(&format!("EXPOSE {}\n\n", self.config.container.port));
488
489        // Health check
490        dockerfile.push_str(&format!(
491            "HEALTHCHECK --interval={}s --timeout={}s --start-period=30s --retries={} \\\n",
492            self.config.health_check.interval_seconds,
493            self.config.health_check.timeout_seconds,
494            self.config.health_check.healthy_threshold
495        ));
496        dockerfile.push_str(&format!(
497            "    CMD curl -f http://localhost:{}{} || exit 1\n\n",
498            self.config.container.port, self.config.health_check.path
499        ));
500
501        // Entry point
502        dockerfile.push_str("CMD ");
503        dockerfile.push_str(
504            &serde_json::to_string(&self.config.container.entrypoint)
505                .map_err(|e| TorshError::SerializationError(e.to_string()))?,
506        );
507        dockerfile.push('\n');
508
509        Ok(dockerfile)
510    }
511
512    /// Generate inference server code
513    fn generate_inference_server(&self, entry: &ModelZooEntry) -> Result<String> {
514        let mut server = String::new();
515
516        server.push_str("#!/usr/bin/env python3\n");
517        server.push_str("\"\"\"Inference server for ToRSh FX model deployment.\"\"\"\n\n");
518
519        server.push_str("import json\n");
520        server.push_str("import logging\n");
521        server.push_str("import os\n");
522        server.push_str("import time\n");
523        server.push_str("from typing import Any, Dict, List\n\n");
524        server.push_str("from flask import Flask, request, jsonify\n");
525        server.push_str("import numpy as np\n\n");
526
527        // Configure logging
528        server.push_str(&format!(
529            "logging.basicConfig(level=logging.{})\n",
530            self.config.monitoring.log_level.to_uppercase()
531        ));
532        server.push_str("logger = logging.getLogger(__name__)\n\n");
533
534        // Create Flask app
535        server.push_str("app = Flask(__name__)\n\n");
536
537        // Global metrics tracking
538        server.push_str("# Global metrics\n");
539        server.push_str("request_count = 0\n");
540        server.push_str("total_inference_time = 0.0\n");
541        server.push_str("error_count = 0\n");
542        server.push_str("start_timestamp = time.time()\n\n");
543
544        // Load model
545        server.push_str("# Load model\n");
546        server.push_str("logger.info('Loading model...')\n");
547        server.push_str("with open('model.json', 'r') as f:\n");
548        server.push_str("    model_data = json.load(f)\n");
549        server.push_str("GRAPH = model_data['graph']\n");
550        server.push_str("GRAPH_NODES = {node['id']: node for node in GRAPH['nodes']}\n");
551        server.push_str("GRAPH_EDGES = [tuple(edge) for edge in GRAPH['edges']]\n");
552        server.push_str(&format!(
553            "logger.info('Loaded model: {}')\n\n",
554            entry.metadata.name
555        ));
556
557        // Graph executor. The exported graph is executed for real; operations whose
558        // parameters are not part of the export refuse loudly instead of returning
559        // something that merely looks like a prediction.
560        server.push_str(GRAPH_EXECUTOR_PY);
561
562        // Health check endpoint
563        server.push_str("@app.route('/health', methods=['GET'])\n");
564        server.push_str("def health_check():\n");
565        server.push_str("    return jsonify({'status': 'healthy'})\n\n");
566
567        // Prediction endpoint
568        server.push_str(PREDICT_ENDPOINT_PY);
569
570        // Metrics endpoint
571        if self.config.monitoring.enable_metrics {
572            server.push_str("@app.route('/metrics', methods=['GET'])\n");
573            server.push_str("def metrics():\n");
574            server.push_str("    \"\"\"Return comprehensive server and model metrics.\"\"\"\n");
575            server.push_str(
576                "    global request_count, total_inference_time, error_count, start_timestamp\n",
577            );
578            server.push_str("    \n");
579            server.push_str("    uptime = time.time() - start_timestamp\n");
580            server.push_str("    avg_inference_time = (total_inference_time / request_count) if request_count > 0 else 0\n");
581            server.push_str("    \n");
582            server.push_str("    metrics_data = {\n");
583            server.push_str("        'server': {\n");
584            server.push_str("            'uptime_seconds': uptime,\n");
585            server.push_str("            'uptime_hours': uptime / 3600,\n");
586            server.push_str("            'start_time': start_timestamp\n");
587            server.push_str("        },\n");
588            server.push_str("        'requests': {\n");
589            server.push_str("            'total': request_count,\n");
590            server.push_str("            'errors': error_count,\n");
591            server.push_str("            'success_rate': ((request_count - error_count) / request_count * 100) if request_count > 0 else 100,\n");
592            server.push_str(
593                "            'requests_per_second': request_count / uptime if uptime > 0 else 0\n",
594            );
595            server.push_str("        },\n");
596            server.push_str("        'inference': {\n");
597            server.push_str("            'total_time_seconds': total_inference_time,\n");
598            server.push_str("            'average_time_ms': avg_inference_time * 1000,\n");
599            server.push_str("            'throughput': request_count / total_inference_time if total_inference_time > 0 else 0\n");
600            server.push_str("        },\n");
601            server.push_str("        'system': {\n");
602            server.push_str("            'memory_usage_mb': __import__('psutil').Process().memory_info().rss / 1024 / 1024 if __import__('importlib').util.find_spec('psutil') else 0,\n");
603            server.push_str("            'cpu_percent': __import__('psutil').Process().cpu_percent() if __import__('importlib').util.find_spec('psutil') else 0\n");
604            server.push_str("        }\n");
605            server.push_str("    }\n");
606            server.push_str("    \n");
607            server.push_str("    return jsonify(metrics_data)\n\n");
608        }
609
610        // Main
611        server.push_str("if __name__ == '__main__':\n");
612        server.push_str(&format!(
613            "    app.run(host='0.0.0.0', port={}, debug=False)\n",
614            self.config.container.port
615        ));
616
617        Ok(server)
618    }
619
620    /// Generate requirements.txt
621    fn generate_requirements(&self) -> Result<String> {
622        let mut requirements = String::new();
623
624        requirements.push_str("# Core dependencies\n");
625        requirements.push_str("flask>=2.0.0\n");
626        requirements.push_str("numpy>=1.20.0\n");
627        requirements.push_str("torch>=2.0.0\n\n");
628
629        requirements.push_str("# Additional packages\n");
630        for pkg in &self.config.container.python_packages {
631            requirements.push_str(&format!("{}\n", pkg));
632        }
633
634        if self.config.monitoring.enable_metrics {
635            requirements.push_str("\n# Monitoring\n");
636            requirements.push_str("prometheus-client>=0.14.0\n");
637        }
638
639        if self.config.monitoring.enable_tracing {
640            requirements.push_str("opentelemetry-api>=1.0.0\n");
641            requirements.push_str("opentelemetry-sdk>=1.0.0\n");
642        }
643
644        Ok(requirements)
645    }
646
647    /// Generate platform-specific configuration
648    fn generate_platform_config(&self, _entry: &ModelZooEntry) -> Result<serde_json::Value> {
649        match &self.config.platform {
650            CloudPlatform::AWS { service, .. } => self.generate_aws_config(service),
651            CloudPlatform::GCP { service, .. } => self.generate_gcp_config(service),
652            CloudPlatform::Azure { service, .. } => self.generate_azure_config(service),
653            CloudPlatform::Custom { .. } => Ok(serde_json::json!({"type": "custom"})),
654        }
655    }
656
657    /// Generate AWS-specific configuration
658    fn generate_aws_config(&self, service: &AWSService) -> Result<serde_json::Value> {
659        match service {
660            AWSService::SageMaker {
661                instance_type,
662                endpoint_name,
663            } => Ok(serde_json::json!({
664                "service": "sagemaker",
665                "instance_type": instance_type,
666                "endpoint_name": endpoint_name,
667                "resources": {
668                    "initial_instance_count": 1,
669                }
670            })),
671            AWSService::Lambda {
672                runtime,
673                memory_mb,
674                timeout_seconds,
675            } => Ok(serde_json::json!({
676                "service": "lambda",
677                "runtime": runtime,
678                "memory_mb": memory_mb,
679                "timeout_seconds": timeout_seconds,
680            })),
681            AWSService::ECS {
682                cluster_name,
683                task_definition,
684            } => Ok(serde_json::json!({
685                "service": "ecs",
686                "cluster_name": cluster_name,
687                "task_definition": task_definition,
688            })),
689            AWSService::EKS {
690                cluster_name,
691                namespace,
692            } => Ok(serde_json::json!({
693                "service": "eks",
694                "cluster_name": cluster_name,
695                "namespace": namespace,
696            })),
697        }
698    }
699
700    /// Generate GCP-specific configuration
701    fn generate_gcp_config(&self, service: &GCPService) -> Result<serde_json::Value> {
702        match service {
703            GCPService::VertexAI {
704                model_name,
705                machine_type,
706            } => Ok(serde_json::json!({
707                "service": "vertex_ai",
708                "model_name": model_name,
709                "machine_type": machine_type,
710            })),
711            GCPService::CloudRun {
712                service_name,
713                memory_mb,
714                max_instances,
715            } => Ok(serde_json::json!({
716                "service": "cloud_run",
717                "service_name": service_name,
718                "memory_mb": memory_mb,
719                "max_instances": max_instances,
720            })),
721            GCPService::GKE {
722                cluster_name,
723                namespace,
724            } => Ok(serde_json::json!({
725                "service": "gke",
726                "cluster_name": cluster_name,
727                "namespace": namespace,
728            })),
729            GCPService::CloudFunctions {
730                function_name,
731                runtime,
732                memory_mb,
733            } => Ok(serde_json::json!({
734                "service": "cloud_functions",
735                "function_name": function_name,
736                "runtime": runtime,
737                "memory_mb": memory_mb,
738            })),
739        }
740    }
741
742    /// Generate Azure-specific configuration
743    fn generate_azure_config(&self, service: &AzureService) -> Result<serde_json::Value> {
744        match service {
745            AzureService::AzureML {
746                workspace_name,
747                endpoint_name,
748            } => Ok(serde_json::json!({
749                "service": "azure_ml",
750                "workspace_name": workspace_name,
751                "endpoint_name": endpoint_name,
752            })),
753            AzureService::AzureFunctions {
754                function_app_name,
755                runtime,
756            } => Ok(serde_json::json!({
757                "service": "azure_functions",
758                "function_app_name": function_app_name,
759                "runtime": runtime,
760            })),
761            AzureService::AKS {
762                cluster_name,
763                namespace,
764            } => Ok(serde_json::json!({
765                "service": "aks",
766                "cluster_name": cluster_name,
767                "namespace": namespace,
768            })),
769            AzureService::ACI {
770                container_group_name,
771                cpu_cores,
772                memory_gb,
773            } => Ok(serde_json::json!({
774                "service": "aci",
775                "container_group_name": container_group_name,
776                "cpu_cores": cpu_cores,
777                "memory_gb": memory_gb,
778            })),
779        }
780    }
781
782    /// Generate deployment script
783    fn generate_deployment_script(&self) -> Result<String> {
784        let mut script = String::new();
785
786        script.push_str("#!/bin/bash\n");
787        script.push_str("# Deployment script for ToRSh FX model\n\n");
788        script.push_str("set -e\n\n");
789
790        script.push_str("echo 'Building Docker image...'\n");
791        script.push_str(&format!("docker build -t {} .\n\n", self.config.name));
792
793        match &self.config.platform {
794            CloudPlatform::AWS { region, .. } => {
795                script.push_str("echo 'Deploying to AWS...'\n");
796                script.push_str(&format!("export AWS_REGION={}\n", region));
797                script.push_str("# Add AWS-specific deployment commands here\n\n");
798            }
799            CloudPlatform::GCP {
800                project_id, region, ..
801            } => {
802                script.push_str("echo 'Deploying to GCP...'\n");
803                script.push_str(&format!("export GCP_PROJECT={}\n", project_id));
804                script.push_str(&format!("export GCP_REGION={}\n", region));
805                script.push_str("# Add GCP-specific deployment commands here\n\n");
806            }
807            CloudPlatform::Azure {
808                subscription_id, ..
809            } => {
810                script.push_str("echo 'Deploying to Azure...'\n");
811                script.push_str(&format!(
812                    "export AZURE_SUBSCRIPTION_ID={}\n",
813                    subscription_id
814                ));
815                script.push_str("# Add Azure-specific deployment commands here\n\n");
816            }
817            CloudPlatform::Custom { endpoint, .. } => {
818                script.push_str("echo 'Deploying to custom platform...'\n");
819                script.push_str(&format!("export ENDPOINT={}\n", endpoint));
820                script.push_str("# Add custom deployment commands here\n\n");
821            }
822        }
823
824        script.push_str("echo 'Deployment complete!'\n");
825
826        Ok(script)
827    }
828}
829
830/// Deployment package
831#[derive(Debug, Clone)]
832pub struct DeploymentPackage {
833    /// Package path
834    pub path: PathBuf,
835    /// Deployment configuration
836    pub config: DeploymentConfig,
837    /// Model metadata
838    pub metadata: ModelMetadata,
839}
840
841impl DeploymentPackage {
842    /// Get package path
843    pub fn path(&self) -> &Path {
844        &self.path
845    }
846
847    /// Get configuration
848    pub fn config(&self) -> &DeploymentConfig {
849        &self.config
850    }
851
852    /// Get metadata
853    pub fn metadata(&self) -> &ModelMetadata {
854        &self.metadata
855    }
856}
857
858/// Default configurations for common deployment scenarios
859impl DeploymentConfig {
860    /// Create a default AWS SageMaker configuration
861    pub fn aws_sagemaker(name: String, region: String) -> Self {
862        Self {
863            name,
864            platform: CloudPlatform::AWS {
865                region,
866                service: AWSService::SageMaker {
867                    instance_type: "ml.m5.xlarge".to_string(),
868                    endpoint_name: "torsh-fx-endpoint".to_string(),
869                },
870            },
871            container: ContainerConfig::default(),
872            resources: ResourceRequirements::default(),
873            autoscaling: Some(AutoScalingConfig::default()),
874            environment_variables: HashMap::new(),
875            health_check: HealthCheckConfig::default(),
876            monitoring: MonitoringConfig::default(),
877        }
878    }
879
880    /// Create a default GCP Vertex AI configuration
881    pub fn gcp_vertex_ai(name: String, project_id: String, region: String) -> Self {
882        Self {
883            name,
884            platform: CloudPlatform::GCP {
885                project_id,
886                region,
887                service: GCPService::VertexAI {
888                    model_name: "torsh-fx-model".to_string(),
889                    machine_type: "n1-standard-4".to_string(),
890                },
891            },
892            container: ContainerConfig::default(),
893            resources: ResourceRequirements::default(),
894            autoscaling: Some(AutoScalingConfig::default()),
895            environment_variables: HashMap::new(),
896            health_check: HealthCheckConfig::default(),
897            monitoring: MonitoringConfig::default(),
898        }
899    }
900}
901
902impl Default for ContainerConfig {
903    fn default() -> Self {
904        Self {
905            base_image: "python:3.11-slim".to_string(),
906            python_version: "3.11".to_string(),
907            system_packages: vec!["curl".to_string()],
908            python_packages: Vec::new(),
909            entrypoint: vec!["python".to_string(), "server.py".to_string()],
910            port: 8080,
911        }
912    }
913}
914
915impl Default for ResourceRequirements {
916    fn default() -> Self {
917        Self {
918            cpu_cores: 2.0,
919            memory_gb: 4.0,
920            gpu_count: 0,
921            gpu_type: None,
922            storage_gb: 10,
923        }
924    }
925}
926
927impl Default for AutoScalingConfig {
928    fn default() -> Self {
929        Self {
930            min_instances: 1,
931            max_instances: 10,
932            target_cpu_utilization: 70.0,
933            target_memory_utilization: 80.0,
934            scale_up_cooldown: 60,
935            scale_down_cooldown: 300,
936        }
937    }
938}
939
940impl Default for HealthCheckConfig {
941    fn default() -> Self {
942        Self {
943            path: "/health".to_string(),
944            interval_seconds: 30,
945            timeout_seconds: 10,
946            healthy_threshold: 2,
947            unhealthy_threshold: 3,
948        }
949    }
950}
951
952impl Default for MonitoringConfig {
953    fn default() -> Self {
954        Self {
955            enable_metrics: true,
956            enable_logging: true,
957            enable_tracing: false,
958            metrics_endpoint: None,
959            log_level: "INFO".to_string(),
960            custom_metrics: Vec::new(),
961        }
962    }
963}
964
965#[cfg(test)]
966mod tests {
967    use super::*;
968
969    #[test]
970    fn test_aws_config_generation() {
971        let config =
972            DeploymentConfig::aws_sagemaker("test-deployment".to_string(), "us-east-1".to_string());
973
974        assert_eq!(config.name, "test-deployment");
975        matches!(config.platform, CloudPlatform::AWS { .. });
976    }
977
978    #[test]
979    fn test_gcp_config_generation() {
980        let config = DeploymentConfig::gcp_vertex_ai(
981            "test-deployment".to_string(),
982            "my-project".to_string(),
983            "us-central1".to_string(),
984        );
985
986        assert_eq!(config.name, "test-deployment");
987        matches!(config.platform, CloudPlatform::GCP { .. });
988    }
989
990    #[test]
991    fn test_deployment_packager_creation() {
992        let temp_dir = std::env::temp_dir().join("torsh_fx_cloud_deploy_test");
993        let config = DeploymentConfig::aws_sagemaker("test".to_string(), "us-east-1".to_string());
994
995        let result = CloudDeploymentPackager::new(&temp_dir, config);
996        assert!(result.is_ok());
997
998        // Cleanup
999        let _ = fs::remove_dir_all(&temp_dir);
1000    }
1001}
torsh_fx/cloud_deployment.rs

torsh_fx/
cloud_deployment.rs
