The custom-application-profiling CLI can profile by PID on Windows (psutil is cross-platform). The full UI requires `install.bat` which **reboots the system** — too dangerous. Let me use the CLI profiler directly:


Bash

cd "C:/Users/NickV/cubie-verification/edge-ai-sizing-tool/workers/custom-application-profiling" && cat requirements.txt

Check profiler requirements


annotated-types==0.7.0
openvino==2025.4
anyio==4.12.1
certifi==2026.1.4
charset-normalizer==3.4.4
click==8.3.1
fastapi==0.135.3
h11==0.16.0
idna==3.11
psutil==7.1.0
pydantic==2.12.5
pydantic_core==2.41.5
requests==2.33.0
starlette==0.49.1
typing-inspection==0.4.2
typing_extensions==4.15.0
urllib3==2.6.3
uvicorn==0.34.0


Conversation compacted


This session is being continued from a previous conversation that ran out of context. The summary below covers the earlier portion of the conversation.

Summary:
1. Primary Request and Intent:
   The user's overarching goal is to get the Cubie Trust Compiler verified through Intel's AI Edge Application Ready Verification program (Rev 2.1). The conversation spans multiple phases:
   - Previously: Stress testing and red team/blue team drill on the bare-metal code (completed, 82/82 tests pass)
   - Phase 1: Build an FFI bridge so Python can call cubie_admit() (COMPLETED)
   - Phase 2: Build an OpenVINO demo pipeline with policy evaluation (COMPLETED)
   - Phase 4: Generate submission artifacts — diagrams, system info, load test CSVs (COMPLETED)
   - User then asked to consolidate all files into a single folder (COMPLETED)
   - User then asked if the actual Intel Edge AI Sizing Tool was used or the fallback — answer was fallback
   - User explicitly requested: "yes use the actual sizing tool" — THIS IS THE CURRENT ACTIVE REQUEST

2. Key Technical Concepts:
   - Cubie Trust Compiler: formally verified AI workload admission gate, ~895 ns/call, 82 integration tests
   - FFI Bridge: Rust cdylib (cubie_ffi.dll, 93KB) exposing cubie_admit via C ABI to Python ctypes
   - CubieRequest: #[repr(C)] 40-byte struct with 6 face booleans, HMAC fields, key_slot, peer_id, adapter_id
   - FIPS 198-1 HMAC-SHA256 capability verification with eFuse key slots
   - Intel Edge AI Sizing Tool: Node.js frontend (port 8080) + Python FastAPI workers (ports 5000-6000)
   - Custom Application Profiling: CLI-based profiler using psutil, xpumcli, sysfs NPU — profiles by PID or app launch
   - install.bat REBOOTS THE SYSTEM at line 373 — dangerous to run without user approval
   - The custom profiler's `main.py` supports PID-attach mode which should be cross-platform via psutil
   - The profiler uses Linux-specific paths (/proc/cpuinfo, sysfs NPU, xpumcli) that gracefully degrade on Windows
   - System: Intel Core Ultra 9 285HX, 24 cores, 64GB RAM, RTX 5090, OpenVINO 2025.4.1, Windows 11, Rust 1.95.0, Python 3.14.2

3. Files and Code Sections:

   - **`C:\Users\NickV\cubie-verification\Cargo.toml`** (workspace root, MODIFIED)
     - Added `"cubie-ffi"` to workspace members
     ```
     members = ["bare-metal", "bare-metal-tests", "cubie-ffi"]
     ```

   - **`C:\Users\NickV\cubie-verification\cubie-ffi\Cargo.toml`** (CREATED)
     - cdylib crate for shared library output
     ```toml
     [package]
     name = "cubie-ffi"
     version = "0.1.0"
     edition = "2021"
     [lib]
     crate-type = ["cdylib"]
     [dependencies]
     cubie-bare-metal = { path = "../bare-metal", features = ["std", "mock"] }
     ```

   - **`C:\Users\NickV\cubie-verification\cubie-ffi\src\lib.rs`** (CREATED)
     - FFI bridge re-exporting cubie_admit + mock helpers
     - Key exports: `cubie_mock_setup`, `cubie_mock_set_efuse_key`, `cubie_mock_set_nvlink`, `cubie_mock_set_pcie_bw`, `cubie_clear_adapter_cache`, `cubie_compute_hmac`, `cubie_create_valid_request`
     - Uses `#[used] static _FORCE_LINK` to ensure cubie_admit symbol is linked into cdylib

   - **`C:\Users\NickV\cubie-verification\cubie-ffi\include\cubie.h`** (CREATED)
     - C header documenting the ABI with `_Static_assert(sizeof(CubieRequest) == 40)`
     - Uses `_Bool` for boolean fields, documents full struct layout with offsets

   - **`C:\Users\NickV\cubie-verification\pipeline\__init__.py`** (CREATED)
     - Package init for pipeline module

   - **`C:\Users\NickV\cubie-verification\pipeline\cubie_gate.py`** (CREATED)
     - Python ctypes wrapper with CubieRequest Structure mirroring the Rust layout
     - CubieGate class with: `admit()`, `create_test_request()`, `compute_hmac()`, `set_nvlink()`, `set_pcie_bw()`, `clear_cache()`, `benchmark()`
     - Auto-discovers DLL/SO from target/release or CUBIE_LIB_PATH env var
     - DEFAULT_TEST_KEY = bytes([0x42] * 32)

   - **`C:\Users\NickV\cubie-verification\pipeline\policy.py`** (CREATED)
     - 6-face policy evaluator: WHO (API key), WHAT (model+device approval), HOW (config bounds), WHERE (node auth), WHEN (lease validity), WHY (governance)
     - WorkloadRequest dataclass mirrors Intel Edge Sizing Tool fields
     - PolicyResult includes per-face verdicts and timing
     - Uses hashlib.sha256 for request_hash computation

   - **`C:\Users\NickV\cubie-verification\pipeline\models.json`** (CREATED)
     - Approved model registry: 5 models (yolo11n, yolo-v8, ssd_mobilenet_v2, TinyLlama, resnet-50-tf)
     - 3 allowed API keys, 3 authorized nodes, max_priority=2

   - **`C:\Users\NickV\cubie-verification\pipeline\cubie_openvino_demo.py`** (CREATED)
     - FastAPI server: POST /admit, POST /launch, GET /metrics, GET /health
     - Metrics tracking: total requests, allow/deny counts, latency stats, deny reasons
     - Optional OpenVINO integration on /launch route

   - **`C:\Users\NickV\cubie-verification\pipeline\load_profiles.py`** (CREATED)
     - Three load levels: light (1 req/s), medium (5 req/s), heavy (max throughput)
     - Heavy includes 10% intentionally bad requests to test deny path
     - Outputs per-request CSV with timing data
     - Calculates avg/min/max/p50/p99 latencies

   - **`C:\Users\NickV\cubie-verification\pipeline\resource_monitor.py`** (CREATED)
     - Samples CPU (psutil), memory (psutil), GPU (xpumcli or sysfs), at 1s intervals
     - 5s idle baseline subtraction, CSV output
     - `get_system_info()` captures platform details (Windows: wmic for CPU brand)

   - **`C:\Users\NickV\cubie-verification\pipeline\generate_diagrams.py`** (CREATED)
     - Generates pipeline_block_diagram.png and verification_architecture.png using matplotlib
     - Pipeline diagram: Workload Request → Policy Evaluator → cubie_admit → OpenVINO → Results, with DENY path
     - Architecture diagram: 6 layers from Coq/Lean/Verus down to Python Pipeline

   - **`C:\Users\NickV\cubie-verification\pipeline\requirements.txt`** (CREATED)
     - fastapi>=0.110, uvicorn>=0.29, psutil>=5.9, matplotlib>=3.7, numpy>=1.24

   - **`C:\Users\NickV\cubie-verification\artifacts\capture_system_info.ps1`** (CREATED, then FIXED)
     - PowerShell script capturing CPU, RAM, OS, GPU, Rust, Python, OpenVINO versions
     - Em dash encoding issue fixed

   - **`C:\Users\NickV\cubie-verification\artifacts\pipeline_description.md`** (CREATED)
     - Narrative for Intel submission form

   - **Generated artifacts** in `artifacts/`:
     - `pipeline_block_diagram.png` (88KB), `verification_architecture.png` (134KB)
     - `system_info.txt` (captured system details)
     - `load_light.csv`, `load_medium.csv`, `load_heavy.csv` (40MB for heavy)

   - **`C:\Users\NickV\cubie-verification\intel-edge-submission\`** (CREATED)
     - 38 files: all artifacts, pipeline Python, FFI source, bare-metal source, test files, built DLL

   - **`C:\Users\NickV\cubie-verification\edge-ai-sizing-tool\`** (CLONED)
     - Intel's official sizing tool from GitHub
     - Key files read: README.md, install.bat, start.bat, docs/CUSTOM_APPLICATION_PROFILING.md, workers/custom-application-profiling/main.py (2480 lines), workers/custom-application-profiling/requirements.txt

4. Errors and Fixes:
   - **Em dash encoding error in capture_system_info.ps1**: PowerShell couldn't parse the `—` (em dash) character. Fixed by replacing with `-` (regular hyphen) in both the comment line and the string literal.
   - **install.bat contains system reboot**: Discovered `shutdown /r /t 0` at line 373 of install.bat. Did NOT run it — too dangerous. Pivoting to CLI profiler approach instead.
   - **Custom Application Profiling is "Linux OS only"**: Per the docs. However, the PID-attach mode uses psutil which is cross-platform, so it may work on Windows with degraded GPU/NPU monitoring.

5. Problem Solving:
   - Successfully built FFI bridge from Rust bare-metal library to Python via ctypes (895 ns/call)
   - Successfully created policy evaluator bridging real workload properties to CubieRequest
   - Load testing showed 145K req/s at heavy load, all 82 Rust tests pass
   - System info captured showing full Intel hardware stack (Core Ultra 9 285HX, RTX 5090, OpenVINO 2025.4.1)
   - Currently investigating how to run Intel's actual sizing tool on Windows without the dangerous install.bat

6. All User Messages:
   - (From prior session summary): "we need to stress test this and make sure what you are reporting is real since i dont know if you might of done pseudo code to allow it to run so fast..." — detailed request for red/blue team drill + verification tools
   - "please duplicate all of the files from the intel edge test into a single folder including all diagrams logs and so forth"
   - "did the sizing tool work or did you do the fallback"
   - "yes use the actual sizing tool"

7. Pending Tasks:
   - **ACTIVE: Run the actual Intel Edge AI Sizing Tool** — User explicitly said "yes use the actual sizing tool"
   - The CLI profiler at `edge-ai-sizing-tool/workers/custom-application-profiling/main.py` can profile by PID
   - Need to: install profiler requirements, start Cubie load test as background process, profile it by PID, save JSON output
   - Remaining hardening items from red/blue team drill (P0: HMAC extension to 128-bit, theorem rewrites; P1: static mut fix, cache TTL, quantum terminology strip; verification tools: Crux-MIR, Aeneas, Creusot, cargo-miri) — deferred

8. Current Work:
   I was in the process of setting up and running the actual Intel Edge AI Sizing Tool. I had:
   - Cloned the repo to `C:\Users\NickV\cubie-verification\edge-ai-sizing-tool\`
   - Read the README, install.bat, start.bat, custom profiling docs, and the full `main.py` profiler (2480 lines)
   - Read the profiler's `requirements.txt`
   - Determined that install.bat is too dangerous (reboots system)
   - Determined the CLI profiler's PID-attach mode should work on Windows via psutil
   - Was about to: install the profiler's requirements, start Cubie load test as background process, run `main.py profile --pid <PID> --name "cubie_gate" --duration 60 --output profile.json`

9. Optional Next Step:
   Continue with the user's explicit request: "yes use the actual sizing tool"

   The approach is to use the CLI profiler from `edge-ai-sizing-tool/workers/custom-application-profiling/main.py` in PID-attach mode:
   1. Install requirements (psutil already installed, may need fastapi for the profiler but we have it)
   2. Start the Cubie heavy load test as a background Python process
   3. Get its PID
   4. Run `python main.py profile --pid <PID> --name "cubie_trust_compiler" --duration 60 --output C:/Users/NickV/cubie-verification/artifacts/sizing_tool_profile.json`
   5. Copy the JSON output to the intel-edge-submission folder

   Note: The full UI (http://localhost:8080) requires running install.bat which reboots the system. Should confirm with the user whether the CLI profiler approach is acceptable, or if they want to run install.bat manually with admin privileges.

If you need specific details from before compaction (like exact code snippets, error messages, or content you generated), read the full transcript at: C:\Users\NickV\.claude\projects\C--Users-NickV\bfc2bfbf-6683-4826-bf18-edd758f725bb.jsonl
Continue the conversation from where it left off without asking the user any further questions. Resume directly — do not acknowledge the summary, do not recap what was happening, do not preface with "I'll continue" or similar. Pick up the last task as if the break never happened.


## plugin:context7:context7
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Do not use for: refactoring, writing scripts from scratch, debugging business logic, code review, or general programming concepts.

## plugin:serena:serena
CRITICAL: Before starting to work on a coding task, call the `initial_instructions` tool to read the 'Serena Instructions Manual'.


Let me check the profiler's requirements and set up the environment.


1	annotated-types==0.7.0
2	openvino==2025.4
3	anyio==4.12.1
4	certifi==2026.1.4
5	charset-normalizer==3.4.4
6	click==8.3.1
7	fastapi==0.135.3
8	h11==0.16.0
9	idna==3.11
10	psutil==7.1.0
11	pydantic==2.12.5
12	pydantic_core==2.41.5
13	requests==2.33.0
14	starlette==0.49.1
15	typing-inspection==0.4.2
16	typing_extensions==4.15.0
17	urllib3==2.6.3
18	uvicorn==0.34.0
19


1	#!/usr/bin/env python3
2
3	# Copyright (C) 2025 Intel Corporation
4	# SPDX-License-Identifier: Apache-2.0
5
6	"""
7	Platform Profiler - Application Resource Utilization Analysis
8	==============================================================
9	QUICK START:
10	    # Check platform status
11	    python3 main.py status
12
13	    # Profile your application (supports Python, Node.js, binaries, etc.)
14	    python3 main.py profile --app "python3 customer_sample_app.py" --name "my_app"
15
16	USAGE EXAMPLES:
17	    # Python application
18	    python3 main.py profile \\
19	        --app "python3 customer_sample_app.py" \\
20	        --name "sample_app" \\
21	        --duration 30 \\
22	        --output profile.json
23
24	    # Node.js application
25	    python3 main.py profile --app "node server.js" --name "node_app" --duration 60
26
27	    # Binary/executable
28	    python3 main.py profile --app "./my_program --arg1 --arg2" --name "my_program"
29
30	    # Attach to existing process
31	    python3 main.py profile --pid 12345 --name "existing_app"
32
33	For full documentation, run: python3 main.py --help
34	"""
35
36	import time
37	import subprocess
38	import sys
39	import json
40	import os
41	import re
42	import uuid
43	import shlex
44	from typing import Dict, List, Optional, Tuple, Any
45	from dataclasses import dataclass, asdict, field
46	from pathlib import Path
47	from datetime import datetime
48	import signal
49
50	# FastAPI imports
51	try:
52	    from fastapi import FastAPI, HTTPException
53	    from fastapi.middleware.cors import CORSMiddleware
54	    import uvicorn
55
56	    FASTAPI_AVAILABLE = True
57	except ImportError:
58	    FASTAPI_AVAILABLE = False
59
60	# Import bottleneck analyzer from separate module
61	from bottleneck_analyzer import BottleneckAnalyzer, Bottleneck, BottleneckAnalysis
62
63	# Try to import psutil, provide helpful error if missing
64	try:
65	    import psutil
66	except ImportError:
67	    print("ERROR: psutil module not found!", file=sys.stderr)
68	    print("", file=sys.stderr)
69	    print("Please install it using ONE of these methods:", file=sys.stderr)
70	    print("  1. pip install psutil", file=sys.stderr)
71	    print("  2. sudo apt-get install python3-psutil", file=sys.stderr)
72	    print(
73	        "  3. Create venv: python3 -m venv venv && source venv/bin/activate && pip install psutil",
74	        file=sys.stderr,
75	    )
76	    sys.exit(1)
77
78
79	# ============================================================================
80	# Security - Input Validation

#!/usr/bin/env python3

# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

"""
Platform Profiler - Application Resource Utilization Analysis
==============================================================
QUICK START:
    # Check platform status
    python3 main.py status

    # Profile your application (supports Python, Node.js, binaries, etc.)
    python3 main.py profile --app "python3 customer_sample_app.py" --name "my_app"

USAGE EXAMPLES:
    # Python application
    python3 main.py profile \\
        --app "python3 customer_sample_app.py" \\
        --name "sample_app" \\
        --duration 30 \\
        --output profile.json

    # Node.js application
    python3 main.py profile --app "node server.js" --name "node_app" --duration 60

    # Binary/executable
    python3 main.py profile --app "./my_program --arg1 --arg2" --name "my_program"

    # Attach to existing process
    python3 main.py profile --pid 12345 --name "existing_app"

For full documentation, run: python3 main.py --help
"""

import time
import subprocess
import sys
import json
import os
import re
import uuid
import shlex
from typing import Dict, List, Optional, Tuple, Any
from dataclasses import dataclass, asdict, field
from pathlib import Path
from datetime import datetime
import signal

# FastAPI imports
try:
    from fastapi import FastAPI, HTTPException
    from fastapi.middleware.cors import CORSMiddleware
    import uvicorn

    FASTAPI_AVAILABLE = True
except ImportError:
    FASTAPI_AVAILABLE = False

# Import bottleneck analyzer from separate module
from bottleneck_analyzer import BottleneckAnalyzer, Bottleneck, BottleneckAnalysis

# Try to import psutil, provide helpful error if missing
try:
    import psutil
except ImportError:
    print("ERROR: psutil module not found!", file=sys.stderr)
    print("", file=sys.stderr)
    print("Please install it using ONE of these methods:", file=sys.stderr)
    print("  1. pip install psutil", file=sys.stderr)
    print("  2. sudo apt-get install python3-psutil", file=sys.stderr)
    print(
        "  3. Create venv: python3 -m venv venv && source venv/bin/activate && pip install psutil",
        file=sys.stderr,
    )
    sys.exit(1)

# ============================================================================
# Security - Input Validation


Let me check the bottleneck_analyzer and the CLI entry point structure.


2400	    profile_parser.add_argument(
2401	        "--duration",
2402	        type=float,
2403	        default=30.0,
2404	        help="Profile duration in seconds (default: 30)",
2405	    )
2406	    profile_parser.add_argument(
2407	        "--output", type=str, help="Output JSON file (optional)"
2408	    )
2409
2410	    args = parser.parse_args()
2411
2412	    if not args.command:
2413	        print_banner()
2414	        parser.print_help()
2415	        return
2416
2417	    # SECURITY: Validate command is one of the allowed subcommands (taint barrier)
2418	    # This acts as a taint barrier for static analysis tools
2419	    allowed_commands = ["profile", "status"]
2420	    if args.command not in allowed_commands:
2421	        print(f"ERROR: Invalid command '{args.command}'")
2422	        return
2423
2424	    # Execute validated command
2425	    # Note: Additional validation occurs inside each function for command-specific fields
2426	    if args.command == "profile":
2427	        # SECURITY: Break taint chain by validating individual fields
2428	        # Extract and validate each field to prevent taint propagation
2429	        validated_args = type(
2430	            "obj",
2431	            (object,),
2432	            {
2433	                "command": str(args.command),  # Already validated via whitelist
2434	                "app": args.app if args.app is None else str(args.app),
2435	                "pid": args.pid if args.pid is None else int(args.pid),
2436	                "name": args.name if args.name is None else str(args.name),
2437	                "baseline": float(args.baseline) if hasattr(args, "baseline") else 5.0,
2438	                "duration": float(args.duration) if hasattr(args, "duration") else 30.0,
2439	                "output": (
2440	                    args.output
2441	                    if not hasattr(args, "output") or args.output is None
2442	                    else str(args.output)
2443	                ),
2444	            },
2445	        )()
2446	        run_profile(validated_args)
2447	    elif args.command == "status":
2448	        run_status(args)
2449
2450
2451	if __name__ == "__main__":
2452	    # Check if running in API mode
2453	    if len(sys.argv) > 1 and sys.argv[1] == "api":
2454	        if not FASTAPI_AVAILABLE:
2455	            print("ERROR: FastAPI not available!", file=sys.stderr)
2456	            print("Install with: pip install fastapi uvicorn", file=sys.stderr)
2457	            sys.exit(1)
2458
2459	        # Remove 'api' from argv so parse_api_arguments works correctly
2460	        # sys.argv.pop(1)
2461	        sys.argv = [sys.argv[0]] + sys.argv[
2462	            2:
2463	        ]  # Keep script name, remove 'api', keep rest
2464	        args = parse_api_arguments()
2465
2466	        print(f"Starting Platform Profiler API on {args.host}:{args.port}")
2467	        print(f"API endpoints:")
2468	        print(f"  GET  http://{args.host}:{args.port}/api/status")
2469	        print(f"  POST http://{args.host}:{args.port}/api/profile-start")
2470	        print(f"  GET  http://{args.host}:{args.port}/api/profile-monitor")
2471	        print(
2472	            f"  GET  http://{args.host}:{args.port}/api/profile/quick?pid=<PID>&duration=<seconds>"
2473	        )
2474	        print(f"\nDocs: http://{args.host}:{args.port}/docs")
2475
2476	        uvicorn.run(app, host=args.host, port=args.port, reload=args.reload)
2477	    else:
2478	        # Run CLI mode
2479	        main()

    profile_parser.add_argument(
        "--duration",
        type=float,
        default=30.0,
        help="Profile duration in seconds (default: 30)",
    )
    profile_parser.add_argument(
        "--output", type=str, help="Output JSON file (optional)"
    )

    args = parser.parse_args()

    if not args.command:
        print_banner()
        parser.print_help()
        return

    # SECURITY: Validate command is one of the allowed subcommands (taint barrier)
    # This acts as a taint barrier for static analysis tools
    allowed_commands = ["profile", "status"]
    if args.command not in allowed_commands:
        print(f"ERROR: Invalid command '{args.command}'")
        return

    # Execute validated command
    # Note: Additional validation occurs inside each function for command-specific fields
    if args.command == "profile":
        # SECURITY: Break taint chain by validating individual fields
        # Extract and validate each field to prevent taint propagation
        validated_args = type(
            "obj",
            (object,),
            {
                "command": str(args.command),  # Already validated via whitelist
                "app": args.app if args.app is None else str(args.app),
                "pid": args.pid if args.pid is None else int(args.pid),
                "name": args.name if args.name is None else str(args.name),
                "baseline": float(args.baseline) if hasattr(args, "baseline") else 5.0,
                "duration": float(args.duration) if hasattr(args, "duration") else 30.0,
                "output": (
                    args.output
                    if not hasattr(args, "output") or args.output is None
                    else str(args.output)
                ),
            },
        )()
        run_profile(validated_args)
    elif args.command == "status":
        run_status(args)

if __name__ == "__main__":
    # Check if running in API mode
    if len(sys.argv) > 1 and sys.argv[1] == "api":
        if not FASTAPI_AVAILABLE:
            print("ERROR: FastAPI not available!", file=sys.stderr)
            print("Install with: pip install fastapi uvicorn", file=sys.stderr)
            sys.exit(1)

        # Remove 'api' from argv so parse_api_arguments works correctly
        # sys.argv.pop(1)
        sys.argv = [sys.argv[0]] + sys.argv[
            2:
        ]  # Keep script name, remove 'api', keep rest
        args = parse_api_arguments()

        print(f"Starting Platform Profiler API on {args.host}:{args.port}")
        print(f"API endpoints:")
        print(f"  GET  http://{args.host}:{args.port}/api/status")
        print(f"  POST http://{args.host}:{args.port}/api/profile-start")
        print(f"  GET  http://{args.host}:{args.port}/api/profile-monitor")
        print(
            f"  GET  http://{args.host}:{args.port}/api/profile/quick?pid=<PID>&duration=<seconds>"
        )
        print(f"\nDocs: http://{args.host}:{args.port}/docs")

        uvicorn.run(app, host=args.host, port=args.port, reload=args.reload)
    else:
        # Run CLI mode
        main()


2350	        help="Host to bind service to (default: 127.0.0.1)",
2351	    )
2352	    parser.add_argument(
2353	        "--reload", action="store_true", help="Enable auto reload for development"
2354	    )
2355
2356	    return parser.parse_args()
2357
2358
2359	def main():
2360	    """Main CLI entry point"""
2361	    import argparse
2362
2363	    parser = argparse.ArgumentParser(
2364	        description="Platform Profiler - Application Resource Utilization Analysis",
2365	        formatter_class=argparse.RawDescriptionHelpFormatter,
2366	        epilog="""
2367	EXAMPLES:
2368	  # Check platform status
2369	  %(prog)s status
2370
2371	  # Profile your application
2372	  %(prog)s profile --app "python inference.py" --name "my_app" --duration 30
2373
2374	For more info: https://github.com/intel/...
2375	        """,
2376	    )
2377
2378	    subparsers = parser.add_subparsers(dest="command", help="Command to execute")
2379
2380	    # Status command
2381	    status_parser = subparsers.add_parser("status", help="Show current platform status")
2382
2383	    # Profile command
2384	    profile_parser = subparsers.add_parser("profile", help="Profile application")
2385	    profile_parser.add_argument(
2386	        "--app", type=str, help="Application command to execute"
2387	    )
2388	    profile_parser.add_argument(
2389	        "--pid", type=int, help="PID of running process to profile"
2390	    )
2391	    profile_parser.add_argument(
2392	        "--name", type=str, default="app", help="Application name (default: app)"
2393	    )
2394	    profile_parser.add_argument(
2395	        "--baseline",
2396	        type=float,
2397	        default=5.0,
2398	        help="Baseline duration in seconds (default: 5)",
2399	    )
2400	    profile_parser.add_argument(
2401	        "--duration",
2402	        type=float,
2403	        default=30.0,
2404	        help="Profile duration in seconds (default: 30)",
2405	    )
2406	    profile_parser.add_argument(
2407	        "--output", type=str, help="Output JSON file (optional)"
2408	    )
2409

        help="Host to bind service to (default: 127.0.0.1)",
    )
    parser.add_argument(
        "--reload", action="store_true", help="Enable auto reload for development"
    )

    return parser.parse_args()

def main():
    """Main CLI entry point"""
    import argparse

    parser = argparse.ArgumentParser(
        description="Platform Profiler - Application Resource Utilization Analysis",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
EXAMPLES:
  # Check platform status
  %(prog)s status

  # Profile your application
  %(prog)s profile --app "python inference.py" --name "my_app" --duration 30

For more info: https://github.com/intel/...
        """,
    )

    subparsers = parser.add_subparsers(dest="command", help="Command to execute")

    # Status command
    status_parser = subparsers.add_parser("status", help="Show current platform status")

    # Profile command
    profile_parser = subparsers.add_parser("profile", help="Profile application")
    profile_parser.add_argument(
        "--app", type=str, help="Application command to execute"
    )
    profile_parser.add_argument(
        "--pid", type=int, help="PID of running process to profile"
    )
    profile_parser.add_argument(
        "--name", type=str, default="app", help="Application name (default: app)"
    )
    profile_parser.add_argument(
        "--baseline",
        type=float,
        default=5.0,
        help="Baseline duration in seconds (default: 5)",
    )
    profile_parser.add_argument(
        "--duration",
        type=float,
        default=30.0,
        help="Profile duration in seconds (default: 30)",
    )
    profile_parser.add_argument(
        "--output", type=str, help="Output JSON file (optional)"
    )


Good. Now let me look at the `run_profile` function to understand how PID mode works.


1707:def run_profile(args):

1707:def run_profile(args):


1707	def run_profile(args):
1708	    """Run profiling command"""
1709	    # Validate arguments
1710	    if not args.app and not args.pid:
1711	        print("ERROR: Either --app or --pid must be specified.")
1712	        print("\nOptions:")
1713	        print("  --app 'command'  : Launch and profile a new application")
1714	        print("  --pid 12345      : Attach to and profile an existing process")
1715	        print(
1716	            "\nExample 1: python3 platform_profiler.py profile --app 'python inference.py' --name 'my_app'"
1717	        )
1718	        print(
1719	            "Example 2: python3 platform_profiler.py profile --pid 12345 --name 'my_app'"
1720	        )
1721	        return
1722
1723	    if args.app and args.pid:
1724	        print("ERROR: Cannot specify both --app and --pid. Choose one.")
1725	        return
1726
1727	    print_banner()
1728	    print(f"Profiling Application: {args.name}")
1729	    print("=" * 80)
1730
1731	    orchestrator = ProfilingOrchestrator()
1732
1733	    # Create session
1734	    session = orchestrator.create_session()
1735
1736	    # Establish baseline
1737	    print(f"\nEstablishing baseline ({args.baseline}s)...")
1738	    baseline = orchestrator.establish_baseline(
1739	        session.session_id, duration=args.baseline
1740	    )
1741
1742	    # Profile application
1743	    if args.pid:
1744	        print(f"\nAttaching to PID: {args.pid}")
1745	        app = orchestrator.attach_to_process(session.session_id, args.name, args.pid)
1746	    else:
1747	        # SECURITY: Validate command string to prevent command injection (taint barrier)
1748	        try:
1749	            validated_command = validate_command_string(args.app)
1750	        except ValueError as e:
1751	            print(f"\nERROR: Invalid command: {e}")
1752	            return
1753
1754	        # Validate file paths in the command (universal approach)
1755	        command_parts = validated_command.strip().split()
1756
1757	        # Check each argument that looks like a file path
1758	        for arg in command_parts:
1759	            # Skip flags/options (start with -)
1760	            if arg.startswith("-"):
1761	                continue
1762
1763	            # Check if argument looks like a file path (has extension or path separators)
1764	            if "/" in arg or "\\" in arg or ("." in arg and not arg.startswith(".")):
1765	                # SECURITY: Validate file path to prevent path manipulation (taint barrier)
1766	                try:
1767	                    file_path = validate_file_path(arg)
1768	                except ValueError as e:
1769	                    print(f"\nERROR: Invalid file path '{arg}': {e}")
1770	                    return
1771
1772	                # Validate file exists
1773	                if not file_path.exists():
1774	                    print(f"\nERROR: File not found: {arg}")
1775	                    print(f"  Resolved path: {file_path}")
1776	                    print(f"  Current directory: {Path.cwd()}")
1777	                    print(f"\nPlease check the file path and try again.")
1778	                    return
1779
1780	                if not file_path.is_file():
1781	                    print(f"\nERROR: Path is not a file: {arg}")
1782	                    print(f"  Resolved path: {file_path}")
1783	                    print(f"\nPlease provide a valid file path.")
1784	                    return
1785
1786	                print(f"\n✓ File validation passed: {file_path}")
1787
1788	        print(f"\nLaunching: {validated_command}")
1789	        # Pass validated command (taint removed by validate_command_string)
1790	        app = orchestrator.launch_application(
1791	            session.session_id, args.name, validated_command
1792	        )
1793
1794	    if app.status == "running":
1795	        profile = orchestrator.monitor_application(
1796	            session.session_id, args.name, duration=args.duration
1797	        )
1798
1799	        # Only stop if we launched it (not if we attached to existing PID)
1800	        if args.app:
1801	            orchestrator.stop_application(session.session_id, args.name)
1802	        else:
1803	            print(f"Note: Process {args.pid} is still running (attached mode)")
1804
1805	        # Show results
1806	        print("\n" + "=" * 80)
1807	        print("PROFILING RESULTS")
1808	        print("=" * 80)
1809	        print(f"\nApplication: {args.name}")
1810	        print(f"Duration: {profile.duration:.1f}s")
1811	        print(f"\n{'Resource':<20} {'Average':<20} {'Peak':<20}")
1812	        print("-" * 80)
1813	        print(
1814	            f"{'CPU (normalized)':<20} {profile.cpu_percent_normalized:>6.1f}% {profile.peak_cpu_percent_normalized:>20.1f}%"
1815	        )
1816	        print(
1817	            f"{'  ({profile.logical_cores} cores)':<20} [{profile.cpu_percent:>5.1f}% total] [{profile.peak_cpu_percent:>14.1f}% total]"
1818	        )
1819	        print(
1820	            f"{'Memory':<20} {profile.memory_mb:>10.0f} MB {profile.peak_memory_mb:>15.0f} MB"
1821	        )
1822	        print(
1823	            f"{'GPU Compute':<20} {profile.gpu_compute_percent:>6.1f}% {profile.peak_gpu_compute_percent:>20.1f}%"
1824	        )
1825	        print(
1826	            f"{'GPU Memory':<20} {profile.gpu_memory_mb:>10.0f} MB {profile.peak_gpu_memory_mb:>15.0f} MB"
1827	        )
1828	        print(
1829	            f"{'NPU':<20} {profile.npu_percent:>6.1f}% {profile.peak_npu_percent:>20.1f}%"
1830	        )
1831	        print(
1832	            f"{'Disk Read':<20} {profile.disk_read_mb_per_sec:>8.1f} MB/s {profile.peak_disk_read_mb_per_sec:>16.1f} MB/s"
1833	        )
1834	        print(
1835	            f"{'Disk Write':<20} {profile.disk_write_mb_per_sec:>8.1f} MB/s {profile.peak_disk_write_mb_per_sec:>16.1f} MB/s"
1836	        )
1837
1838	        # Perform bottleneck analysis
1839	        analyzer = BottleneckAnalyzer()
1840	        analysis = analyzer.analyze(profile, baseline)
1841
1842	        # Display bottleneck analysis
1843	        analyzer.print_analysis(analysis)
1844
1845	        # Save to file if requested
1846	        if args.output:
1847	            orchestrator.save_profile_to_json(
1848	                args.output, session, profile, baseline, analysis
1849	            )
1850
1851	    else:
1852	        print(f"\n✗ Failed to launch application: {app.error_message}")
1853
1854
1855	def run_status(args):
1856	    """Show current platform status"""
1857	    orchestrator = ProfilingOrchestrator()
1858
1859	    print("╔" + "═" * 78 + "╗")
1860	    print("║" + " " * 28 + "PLATFORM STATUS" + " " * 35 + "║")
1861	    print("╚" + "═" * 78 + "╝")
1862	    print()
1863
1864	    # CPU
1865	    cpu_info = orchestrator.cpu_monitor.get_cpu_info()
1866	    cpu_util = orchestrator.cpu_monitor.get_overall_utilization()
1867	    print(f"CPU: {cpu_info.model}")
1868	    print(
1869	        f"  Cores: {cpu_info.physical_cores} physical, {cpu_info.logical_threads} logical"
1870	    )
1871	    print(f"  Utilization: {cpu_util:.1f}%")
1872	    print()
1873
1874	    # Memory
1875	    mem = orchestrator.memory_monitor.get_system_memory()
1876	    print(f"Memory:")
1877	    print(f"  Total: {mem.total_mb:,.0f} MB ({mem.total_mb/1024:.1f} GB)")
1878	    print(f"  Used: {mem.used_mb:,.0f} MB ({mem.percent:.1f}%)")
1879	    print(f"  Available: {mem.available_mb:,.0f} MB ({100-mem.percent:.1f}%)")
1880	    print()
1881
1882	    # GPU
1883	    gpus = orchestrator.gpu_monitor.get_all_utilizations()
1884	    if gpus:
1885	        for gpu in gpus:
1886	            print(f"GPU {gpu.device_id}: {gpu.name}")
1887	            print(f"  Compute: {gpu.compute_percent:.1f}%")
1888	            print(
1889	                f"  Memory: {gpu.memory_used_mb:.0f} MB / {gpu.memory_total_mb:.0f} MB ({gpu.memory_percent:.1f}%)"
1890	            )
1891	            print()
1892	    else:
1893	        print("GPU: Not available")
1894	        print()
1895
1896	    # NPU
1897	    if orchestrator.npu_monitor.is_available():
1898	        npu_util = orchestrator.npu_monitor.get_utilization()
1899	        print(f"NPU: Available")
1900	        print(f"  Utilization: {npu_util:.1f}%")
1901	    else:
1902	        print("NPU: Not available")
1903	    print()
1904
1905
1906	# ============================================================================

def run_profile(args):
    """Run profiling command"""
    # Validate arguments
    if not args.app and not args.pid:
        print("ERROR: Either --app or --pid must be specified.")
        print("\nOptions:")
        print("  --app 'command'  : Launch and profile a new application")
        print("  --pid 12345      : Attach to and profile an existing process")
        print(
            "\nExample 1: python3 platform_profiler.py profile --app 'python inference.py' --name 'my_app'"
        )
        print(
            "Example 2: python3 platform_profiler.py profile --pid 12345 --name 'my_app'"
        )
        return

    if args.app and args.pid:
        print("ERROR: Cannot specify both --app and --pid. Choose one.")
        return

    print_banner()
    print(f"Profiling Application: {args.name}")
    print("=" * 80)

    orchestrator = ProfilingOrchestrator()

    # Create session
    session = orchestrator.create_session()

    # Establish baseline
    print(f"\nEstablishing baseline ({args.baseline}s)...")
    baseline = orchestrator.establish_baseline(
        session.session_id, duration=args.baseline
    )

    # Profile application
    if args.pid:
        print(f"\nAttaching to PID: {args.pid}")
        app = orchestrator.attach_to_process(session.session_id, args.name, args.pid)
    else:
        # SECURITY: Validate command string to prevent command injection (taint barrier)
        try:
            validated_command = validate_command_string(args.app)
        except ValueError as e:
            print(f"\nERROR: Invalid command: {e}")
            return

        # Validate file paths in the command (universal approach)
        command_parts = validated_command.strip().split()

        # Check each argument that looks like a file path
        for arg in command_parts:
            # Skip flags/options (start with -)
            if arg.startswith("-"):
                continue

            # Check if argument looks like a file path (has extension or path separators)
            if "/" in arg or "\\" in arg or ("." in arg and not arg.startswith(".")):
                # SECURITY: Validate file path to prevent path manipulation (taint barrier)
                try:
                    file_path = validate_file_path(arg)
                except ValueError as e:
                    print(f"\nERROR: Invalid file path '{arg}': {e}")
                    return

                # Validate file exists
                if not file_path.exists():
                    print(f"\nERROR: File not found: {arg}")
                    print(f"  Resolved path: {file_path}")
                    print(f"  Current directory: {Path.cwd()}")
                    print(f"\nPlease check the file path and try again.")
                    return

                if not file_path.is_file():
                    print(f"\nERROR: Path is not a file: {arg}")
                    print(f"  Resolved path: {file_path}")
                    print(f"\nPlease provide a valid file path.")
                    return

                print(f"\n✓ File validation passed: {file_path}")

        print(f"\nLaunching: {validated_command}")
        # Pass validated command (taint removed by validate_command_string)
        app = orchestrator.launch_application(
            session.session_id, args.name, validated_command
        )

    if app.status == "running":
        profile = orchestrator.monitor_application(
            session.session_id, args.name, duration=args.duration
        )

        # Only stop if we launched it (not if we attached to existing PID)
        if args.app:
            orchestrator.stop_application(session.session_id, args.name)
        else:
            print(f"Note: Process {args.pid} is still running (attached mode)")

        # Show results
        print("\n" + "=" * 80)
        print("PROFILING RESULTS")
        print("=" * 80)
        print(f"\nApplication: {args.name}")
        print(f"Duration: {profile.duration:.1f}s")
        print(f"\n{'Resource':<20} {'Average':<20} {'Peak':<20}")
        print("-" * 80)
        print(
            f"{'CPU (normalized)':<20} {profile.cpu_percent_normalized:>6.1f}% {profile.peak_cpu_percent_normalized:>20.1f}%"
        )
        print(
            f"{'  ({profile.logical_cores} cores)':<20} [{profile.cpu_percent:>5.1f}% total] [{profile.peak_cpu_percent:>14.1f}% total]"
        )
        print(
            f"{'Memory':<20} {profile.memory_mb:>10.0f} MB {profile.peak_memory_mb:>15.0f} MB"
        )
        print(
            f"{'GPU Compute':<20} {profile.gpu_compute_percent:>6.1f}% {profile.peak_gpu_compute_percent:>20.1f}%"
        )
        print(
            f"{'GPU Memory':<20} {profile.gpu_memory_mb:>10.0f} MB {profile.peak_gpu_memory_mb:>15.0f} MB"
        )
        print(
            f"{'NPU':<20} {profile.npu_percent:>6.1f}% {profile.peak_npu_percent:>20.1f}%"
        )
        print(
            f"{'Disk Read':<20} {profile.disk_read_mb_per_sec:>8.1f} MB/s {profile.peak_disk_read_mb_per_sec:>16.1f} MB/s"
        )
        print(
            f"{'Disk Write':<20} {profile.disk_write_mb_per_sec:>8.1f} MB/s {profile.peak_disk_write_mb_per_sec:>16.1f} MB/s"
        )

        # Perform bottleneck analysis
        analyzer = BottleneckAnalyzer()
        analysis = analyzer.analyze(profile, baseline)

        # Display bottleneck analysis
        analyzer.print_analysis(analysis)

        # Save to file if requested
        if args.output:
            orchestrator.save_profile_to_json(
                args.output, session, profile, baseline, analysis
            )

    else:
        print(f"\n✗ Failed to launch application: {app.error_message}")

def run_status(args):
    """Show current platform status"""
    orchestrator = ProfilingOrchestrator()

    print("╔" + "═" * 78 + "╗")
    print("║" + " " * 28 + "PLATFORM STATUS" + " " * 35 + "║")
    print("╚" + "═" * 78 + "╝")
    print()

    # CPU
    cpu_info = orchestrator.cpu_monitor.get_cpu_info()
    cpu_util = orchestrator.cpu_monitor.get_overall_utilization()
    print(f"CPU: {cpu_info.model}")
    print(
        f"  Cores: {cpu_info.physical_cores} physical, {cpu_info.logical_threads} logical"
    )
    print(f"  Utilization: {cpu_util:.1f}%")
    print()

    # Memory
    mem = orchestrator.memory_monitor.get_system_memory()
    print(f"Memory:")
    print(f"  Total: {mem.total_mb:,.0f} MB ({mem.total_mb/1024:.1f} GB)")
    print(f"  Used: {mem.used_mb:,.0f} MB ({mem.percent:.1f}%)")
    print(f"  Available: {mem.available_mb:,.0f} MB ({100-mem.percent:.1f}%)")
    print()

    # GPU
    gpus = orchestrator.gpu_monitor.get_all_utilizations()
    if gpus:
        for gpu in gpus:
            print(f"GPU {gpu.device_id}: {gpu.name}")
            print(f"  Compute: {gpu.compute_percent:.1f}%")
            print(
                f"  Memory: {gpu.memory_used_mb:.0f} MB / {gpu.memory_total_mb:.0f} MB ({gpu.memory_percent:.1f}%)"
            )
            print()
    else:
        print("GPU: Not available")
        print()

    # NPU
    if orchestrator.npu_monitor.is_available():
        npu_util = orchestrator.npu_monitor.get_utilization()
        print(f"NPU: Available")
        print(f"  Utilization: {npu_util:.1f}%")
    else:
        print("NPU: Not available")
    print()

# ============================================================================


ToolSearch

Question: select:TaskCreate,TaskUpdate


Question: select:TaskCreate,TaskUpdate


TaskCreate

Run Intel Edge AI Sizing Tool profiler against Cubie load test

Use the actual Intel Edge AI Sizing Tool's custom application profiler (CLI mode) to profile the Cubie Trust Compiler under load. Steps: install profiler deps, start Cubie load test as background process, run profiler with PID attach, save JSON output to artifacts.

Running Intel Edge AI Sizing Tool profiler


Task #23 created successfully: Run Intel Edge AI Sizing Tool profiler against Cubie load test


Updated task #23 status


First, let me check what's already installed and install the profiler's dependencies.


Bash

cd "C:\Users\NickV\cubie-verification" && python -c "import psutil; print('psutil:', psutil.__version__)" && python -c "import fastapi; print('fastapi:', fastapi.__version__)" 2>&1

Check if profiler dependencies are installed


psutil: 5.9.8
fastapi: 0.129.0


Good, core deps are present. Now let me create a small script that runs the Cubie heavy load test as a long-running process so we can attach the profiler to it.


Write

"""
Cubie Trust Compiler load generator for Intel Edge AI Sizing Tool profiling.
Runs continuous heavy load for a specified duration so the profiler can attach by PID.
"""
import sys
import os
import time
import hashlib
import ctypes

# Add parent directory to path for imports
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from pipeline.cubie_gate import CubieGate, CubieRequest

def run_continuous_load(duration_seconds=120):
    """Run continuous heavy load for profiling."""
    print(f"PID: {os.getpid()}", flush=True)
    print(f"Starting Cubie Trust Compiler heavy load for {duration_seconds}s...", flush=True)

    gate = CubieGate()

    # Create a valid test request
    test_req = gate.create_test_request()

    total_calls = 0
    allow_count = 0
    deny_count = 0
    start = time.perf_counter()
    last_report = start

    while True:
        elapsed = time.perf_counter() - start
        if elapsed >= duration_seconds:
            break

        # Batch of 10000 calls for throughput
        for _ in range(10000):
            result = gate.admit(test_req)
            if result:
                allow_count += 1
            else:
                deny_count += 1
            total_calls += 1

        # Also test deny path (bad HMAC)
        bad_req = CubieRequest(
            who_pass=True, what_pass=True, how_pass=True,
            where_pass=True, when_pass=True, why_pass=True,
            has_valid_lease=True, epoch_fresh=True,
            request_hash=test_req.request_hash,
            capability_hmac=0xDEADBEEF,
            key_slot=0, peer_id=0, adapter_id=0
        )
        for _ in range(1000):
            result = gate.admit(bad_req)
            if result:
                allow_count += 1
            else:
                deny_count += 1
            total_calls += 1

        # Report every 5 seconds
        now = time.perf_counter()
        if now - last_report >= 5.0:
            rate = total_calls / (now - start)
            print(f"  [{elapsed:.0f}s] {total_calls:,} calls, {rate:,.0f} calls/sec, allow={allow_count:,} deny={deny_count:,}", flush=True)
            last_report = now

    elapsed = time.perf_counter() - start
    rate = total_calls / elapsed
    print(f"\nDone: {total_calls:,} total calls in {elapsed:.1f}s = {rate:,.0f} calls/sec", flush=True)
    print(f"  Allow: {allow_count:,}  Deny: {deny_count:,}", flush=True)

if __name__ == "__main__":
    duration = int(sys.argv[1]) if len(sys.argv) > 1 else 120
    run_continuous_load(duration)


File created successfully at: C:\Users\NickV\cubie-verification\pipeline\cubie_load_for_profiler.py

"""
Cubie Trust Compiler load generator for Intel Edge AI Sizing Tool profiling.
Runs continuous heavy load for a specified duration so the profiler can attach by PID.
"""
import sys
import os
import time
import hashlib
import ctypes

# Add parent directory to path for imports
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from pipeline.cubie_gate import CubieGate, CubieRequest

def run_continuous_load(duration_seconds=120):
    """Run continuous heavy load for profiling."""
    print(f"PID: {os.getpid()}", flush=True)
    print(f"Starting Cubie Trust Compiler heavy load for {duration_seconds}s...", flush=True)

    gate = CubieGate()

    # Create a valid test request
    test_req = gate.create_test_request()

    total_calls = 0
    allow_count = 0
    deny_count = 0
    start = time.perf_counter()
    last_report = start

    while True:
        elapsed = time.perf_counter() - start
        if elapsed >= duration_seconds:
            break

        # Batch of 10000 calls for throughput
        for _ in range(10000):
            result = gate.admit(test_req)
            if result:
                allow_count += 1
            else:
                deny_count += 1
            total_calls += 1

        # Also test deny path (bad HMAC)
        bad_req = CubieRequest(
            who_pass=True, what_pass=True, how_pass=True,
            where_pass=True, when_pass=True, why_pass=True,
            has_valid_lease=True, epoch_fresh=True,
            request_hash=test_req.request_hash,
            capability_hmac=0xDEADBEEF,
            key_slot=0, peer_id=0, adapter_id=0
        )
        for _ in range(1000):
            result = gate.admit(bad_req)
            if result:
                allow_count += 1
            else:
                deny_count += 1
            total_calls += 1

        # Report every 5 seconds
        now = time.perf_counter()
        if now - last_report >= 5.0:
            rate = total_calls / (now - start)
            print(f"  [{elapsed:.0f}s] {total_calls:,} calls, {rate:,.0f} calls/sec, allow={allow_count:,} deny={deny_count:,}", flush=True)
            last_report = now

    elapsed = time.perf_counter() - start
    rate = total_calls / elapsed
    print(f"\nDone: {total_calls:,} total calls in {elapsed:.1f}s = {rate:,.0f} calls/sec", flush=True)
    print(f"  Allow: {allow_count:,}  Deny: {deny_count:,}", flush=True)

if __name__ == "__main__":
    duration = int(sys.argv[1]) if len(sys.argv) > 1 else 120
    run_continuous_load(duration)


Now start the load test in the background, capture its PID, then run the Intel profiler against it.


Bash

cd "C:\Users\NickV\cubie-verification" && python pipeline/cubie_load_for_profiler.py 120 &

Start Cubie load test as background process for 120 seconds


<task-notification>
<task-id>b02hz8cmj</task-id>
<tool-use-id>toolu_01VpqPzeu4Yhu1xnw6zRCMCV</tool-use-id>
<output-file>C:\Users\NickV\AppData\Local\Temp\claude\C--Users-NickV\bfc2bfbf-6683-4826-bf18-edd758f725bb\tasks\b02hz8cmj.output</output-file>
<status>completed</status>
<summary>Background command "Start Cubie load test as background process for 120 seconds" completed (exit code 0)</summary>
</task-notification>


Command running in background with ID: b02hz8cmj. Output is being written to: C:\Users\NickV\AppData\Local\Temp\claude\C--Users-NickV\bfc2bfbf-6683-4826-bf18-edd758f725bb\tasks\b02hz8cmj.output


sleep 3 && cat "C:\Users\NickV\AppData\Local\Temp\claude\C--Users-NickV\bfc2bfbf-6683-4826-bf18-edd758f725bb\tasks\b02hz8cmj.output" 2>&1

Get PID from load test output


PID: 55776
Starting Cubie Trust Compiler heavy load for 120s...
  [5s] 4,664,000 calls, 931,640 calls/sec, allow=4,240,000 deny=424,000


Load test is running at PID 55776 (~932K calls/sec). Now run the Intel profiler against it.


cd "C:\Users\NickV\cubie-verification\edge-ai-sizing-tool\workers\custom-application-profiling" && python main.py profile --pid 55776 --name "cubie_trust_compiler" --duration 60 --baseline 5 --output "C:/Users/NickV/cubie-verification/artifacts/intel_sizing_tool_profile.json" 2>&1

Run Intel Edge AI Sizing Tool profiler against Cubie load test PID


Error: Exit code 1
Traceback (most recent call last):
  File "C:\Users\NickV\cubie-verification\edge-ai-sizing-tool\workers\custom-application-profiling\main.py", line 2479, in <module>
    main()
    ~~~~^^
  File "C:\Users\NickV\cubie-verification\edge-ai-sizing-tool\workers\custom-application-profiling\main.py", line 2446, in main
    run_profile(validated_args)
    ~~~~~~~~~~~^^^^^^^^^^^^^^^^
  File "C:\Users\NickV\cubie-verification\edge-ai-sizing-tool\workers\custom-application-profiling\main.py", line 1727, in run_profile
    print_banner()
    ~~~~~~~~~~~~^^
  File "C:\Users\NickV\cubie-verification\edge-ai-sizing-tool\workers\custom-application-profiling\main.py", line 1700, in print_banner
    print("\u2554" + "\u2550" * 78 + "\u2557")
    ~~~~~^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Python314\Lib\encodings\cp1252.py", line 19, in encode
    return codecs.charmap_encode(input,self.errors,encoding_table)[0]
           ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
UnicodeEncodeError: 'charmap' codec can't encode characters in position 0-79: character maps to <undefined>


Unicode encoding issue with Windows console. Let me set the encoding and retry.


cd "C:\Users\NickV\cubie-verification\edge-ai-sizing-tool\workers\custom-application-profiling" && PYTHONIOENCODING=utf-8 python main.py profile --pid 55776 --name "cubie_trust_compiler" --duration 60 --baseline 5 --output "C:/Users/NickV/cubie-verification/artifacts/intel_sizing_tool_profile.json" 2>&1

Run Intel profiler with UTF-8 encoding


╔══════════════════════════════════════════════════════════════════════════════╗
║                    PLATFORM PROFILER v1.0                                    ║
║               CPU/GPU/NPU/Memory Utilization Analysis                        ║
╚══════════════════════════════════════════════════════════════════════════════╝

Profiling Application: cubie_trust_compiler
================================================================================

Establishing baseline (5.0s)...
Establishing baseline for 5.0 seconds...
Baseline established:
  CPU: 18.0%
  Memory: 52637 MB (81.0%)
  GPU: 0 device(s)
  NPU: 0.0% (available: False)
  Disk I/O: 0.0 MB/s read, 0.7 MB/s write

Attaching to PID: 55776
Attaching to process 55776: C:\Python314\python.exe pipeline/cubie_load_for_profiler.py 120
Attached to process tree with 1 process(es):
  Root PID: 55776
Monitoring application 'cubie_trust_compiler' for 60.0 seconds...
  Process tree tracking: enabled

Monitoring completed:
  Process Tree Summary:
    Root PID: 55776
    Final process count: 1
    Peak process count: 1
    Total spawns: 0
    Total exits: 0

  Resource Utilization:
    CPU (24 cores): 4.2% (avg), 4.6% (peak)
    [Total: 4.2% avg, 4.6% peak across all cores]
  Memory: 18 MB (avg), 18 MB (peak)
  GPU Compute: 0.0% (avg), 0.0% (peak)
  GPU Memory: 0 MB (avg), 0 MB (peak)
  NPU: 0.0% (avg), 0.0% (peak)
  Disk I/O: 0.1/0.5 MB/s (avg read/write)
    [Peak: 0.6/4.3 MB/s]
Note: Process 55776 is still running (attached mode)

================================================================================
PROFILING RESULTS
================================================================================

Application: cubie_trust_compiler
Duration: 60.3s

Resource             Average              Peak
--------------------------------------------------------------------------------
CPU (normalized)        4.2%                  4.6%
  ({profile.logical_cores} cores) [  4.2% total] [           4.6% total]
Memory                       18 MB              18 MB
GPU Compute             0.0%                  0.0%
GPU Memory                    0 MB               0 MB
NPU                     0.0%                  0.0%
Disk Read                 0.1 MB/s              0.6 MB/s
Disk Write                0.5 MB/s              4.3 MB/s

================================================================================
BOTTLENECK ANALYSIS
================================================================================

Workload Type: light_workload
Overall Health: HEALTHY
Summary: Workload type: light_workload. No significant bottlenecks detected.

Detected Bottlenecks:
--------------------------------------------------------------------------------

✓ Profile saved to: C:/Users/NickV/cubie-verification/artifacts/intel_sizing_tool_profile.json


The Intel profiler ran successfully and classified the Cubie workload as "HEALTHY" with light CPU usage (4.2% avg on 24 cores). That's expected — ~1M admits/sec in ~895ns each is incredibly efficient. Let me verify the JSON output and also run it against the medium and heavy profiles at different load patterns for the 3-level requirement.


1	{
2	  "session_id": "fbc5736c-293b-43d1-949b-a0deade059f5",
3	  "timestamp": "2026-05-06T19:58:33.958444",
4	  "baseline": {
5	    "timestamp": 1778115453.338948,
6	    "cpu_percent": 17.96,
7	    "cpu_info": {
8	      "model": "Unknown CPU",
9	      "physical_cores": 24,
10	      "logical_threads": 24,
11	      "frequency_mhz": 2800.0
12	    },
13	    "memory_total_mb": 64943.0703125,
14	    "memory_used_mb": 52636.63359375,
15	    "memory_percent": 81.03999999999999,
16	    "gpu_devices": [],
17	    "npu_available": false,
18	    "npu_percent": 0.0,
19	    "disk_read_mb_per_sec": 0.025821557176677678,
20	    "disk_write_mb_per_sec": 0.6848032766316956
21	  },
22	  "application": {
23	    "app_id": "fbc5736c-293b-43d1-949b-a0deade059f5_cubie_trust_compiler",
24	    "name": "cubie_trust_compiler",
25	    "command": "C:\\Python314\\python.exe pipeline/cubie_load_for_profiler.py 120",
26	    "pids": [
27	      55776
28	    ],
29	    "process_name": "python.exe",
30	    "process_tree": {
31	      "55776": {
32	        "pid": 55776,
33	        "ppid": 95964,
34	        "name": "python.exe",
35	        "cmdline": "C:\\Python314\\python.exe pipeline/cubie_load_for_profiler.py 120",
36	        "cpu_percent": 3.8874999999999997,
37	        "memory_mb": 17.57421875,
38	        "memory_percent": 0.027060960723653035,
39	        "num_threads": 1,
40	        "status": "running",
41	        "create_time": 1778115406.7970145,
42	        "children_pids": []
43	      }
44	    },
45	    "root_pid": 55776,
46	    "max_process_count": 1,
47	    "process_spawns": 0,
48	    "process_exits": 0,
49	    "cpu_percent": 4.181666666666667,
50	    "cpu_percent_normalized": 4.181666666666667,
51	    "memory_mb": 17.588975694444443,
52	    "memory_percent": 0.02708368361675531,
53	    "gpu_compute_percent": 0.0,
54	    "gpu_memory_mb": 0.0,
55	    "gpu_memory_percent": 0.0,
56	    "npu_percent": 0.0,
57	    "disk_read_mb_per_sec": 0.10201250367769722,
58	    "disk_write_mb_per_sec": 0.45357808115602843,
59	    "disk_io_percent": 0.11111811696674513,
60	    "peak_cpu_percent": 4.554166666666666,
61	    "peak_cpu_percent_normalized": 4.554166666666666,
62	    "peak_memory_mb": 17.640625,
63	    "peak_gpu_compute_percent": 0.0,
64	    "peak_gpu_memory_mb": 0.0,
65	    "peak_npu_percent": 0.0,
66	    "peak_disk_read_mb_per_sec": 0.6349022561945143,
67	    "peak_disk_write_mb_per_sec": 4.277103404743677,
68	    "peak_disk_io_percent": 0.9179058468642811,
69	    "logical_cores": 24,
70	    "start_time": 1778115453.6083245,
71	    "duration": 60.34993290901184,
72	    "status": "completed",
73	    "error_message": null,
74	    "baseline_warnings": []
75	  },
76	  "bottleneck_analysis": {
77	    "bottlenecks": [
78	      {
79	        "resource": "System",
80	        "severity": "healthy",
81	        "description": "Workload type: light_workload. No significant bottlenecks detected.",
82	        "metrics": {},
83	        "recommendations": []
84	      }
85	    ],
86	    "primary_bottleneck": null,
87	    "workload_type": "light_workload",
88	    "overall_health": "healthy",
89	    "summary": "Workload type: light_workload. No significant bottlenecks detected."
90	  }
91	}


Profile captured. Now let me also run the profiler with the `--app` mode to launch the load test directly — this gives us a cleaner profile with proper baseline. Let me do one for each load level. First, let me create load scripts for light and medium that match Intel's 3-level requirement.


Write

"""Light load: ~1 admission per second, single model, CPU-only."""
import sys, os, time
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from pipeline.cubie_gate import CubieGate, CubieRequest

def main():
    print(f"PID: {os.getpid()}", flush=True)
    gate = CubieGate()
    req = gate.create_test_request()
    duration = int(sys.argv[1]) if len(sys.argv) > 1 else 90
    total = 0
    start = time.perf_counter()
    while time.perf_counter() - start < duration:
        gate.admit(req)
        total += 1
        time.sleep(1.0)  # 1 req/sec
    print(f"Light: {total} calls in {time.perf_counter()-start:.1f}s", flush=True)

if __name__ == "__main__":
    main()


File created successfully at: C:\Users\NickV\cubie-verification\pipeline\cubie_load_light.py

"""Light load: ~1 admission per second, single model, CPU-only."""
import sys, os, time
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from pipeline.cubie_gate import CubieGate, CubieRequest

def main():
    print(f"PID: {os.getpid()}", flush=True)
    gate = CubieGate()
    req = gate.create_test_request()
    duration = int(sys.argv[1]) if len(sys.argv) > 1 else 90
    total = 0
    start = time.perf_counter()
    while time.perf_counter() - start < duration:
        gate.admit(req)
        total += 1
        time.sleep(1.0)  # 1 req/sec
    print(f"Light: {total} calls in {time.perf_counter()-start:.1f}s", flush=True)

if __name__ == "__main__":
    main()


"""Medium load: 5 concurrent workload admission bursts per second."""
import sys, os, time, threading
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from pipeline.cubie_gate import CubieGate, CubieRequest

def worker(gate, req, duration, results, idx):
    count = 0
    start = time.perf_counter()
    while time.perf_counter() - start < duration:
        for _ in range(100):
            gate.admit(req)
            count += 1
        time.sleep(0.01)
    results[idx] = count

def main():
    print(f"PID: {os.getpid()}", flush=True)
    gate = CubieGate()
    req = gate.create_test_request()
    duration = int(sys.argv[1]) if len(sys.argv) > 1 else 90
    n_threads = 5
    results = [0] * n_threads
    threads = []
    for i in range(n_threads):
        t = threading.Thread(target=worker, args=(gate, req, duration, results, i))
        t.start()
        threads.append(t)
    start = time.perf_counter()
    last = start
    while any(t.is_alive() for t in threads):
        time.sleep(5)
        elapsed = time.perf_counter() - start
        total = sum(results)
        print(f"  [{elapsed:.0f}s] {total:,} calls across {n_threads} threads", flush=True)
    for t in threads:
        t.join()
    total = sum(results)
    elapsed = time.perf_counter() - start
    print(f"Medium: {total:,} calls in {elapsed:.1f}s = {total/elapsed:,.0f}/sec", flush=True)

if __name__ == "__main__":
    main()


File created successfully at: C:\Users\NickV\cubie-verification\pipeline\cubie_load_medium.py

"""Medium load: 5 concurrent workload admission bursts per second."""
import sys, os, time, threading
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from pipeline.cubie_gate import CubieGate, CubieRequest

def worker(gate, req, duration, results, idx):
    count = 0
    start = time.perf_counter()
    while time.perf_counter() - start < duration:
        for _ in range(100):
            gate.admit(req)
            count += 1
        time.sleep(0.01)
    results[idx] = count

def main():
    print(f"PID: {os.getpid()}", flush=True)
    gate = CubieGate()
    req = gate.create_test_request()
    duration = int(sys.argv[1]) if len(sys.argv) > 1 else 90
    n_threads = 5
    results = [0] * n_threads
    threads = []
    for i in range(n_threads):
        t = threading.Thread(target=worker, args=(gate, req, duration, results, i))
        t.start()
        threads.append(t)
    start = time.perf_counter()
    last = start
    while any(t.is_alive() for t in threads):
        time.sleep(5)
        elapsed = time.perf_counter() - start
        total = sum(results)
        print(f"  [{elapsed:.0f}s] {total:,} calls across {n_threads} threads", flush=True)
    for t in threads:
        t.join()
    total = sum(results)
    elapsed = time.perf_counter() - start
    print(f"Medium: {total:,} calls in {elapsed:.1f}s = {total/elapsed:,.0f}/sec", flush=True)

if __name__ == "__main__":
    main()


Now run all three profiling sessions. The first background load test should be done soon. Let me run the three Intel profiler sessions sequentially using `--app` mode for clean baselines.


Bash

cd "C:\Users\NickV\cubie-verification" && PYTHONIOENCODING=utf-8 python "edge-ai-sizing-tool/workers/custom-application-profiling/main.py" profile --app "python pipeline/cubie_load_light.py 75" --name "cubie_light_load" --duration 60 --baseline 5 --output "artifacts/intel_profile_light.json" 2>&1

Intel profiler: light load profile (1 req/sec)


Failed to launch application: module 'os' has no attribute 'setsid'
╔══════════════════════════════════════════════════════════════════════════════╗
║                    PLATFORM PROFILER v1.0                                    ║
║               CPU/GPU/NPU/Memory Utilization Analysis                        ║
╚══════════════════════════════════════════════════════════════════════════════╝

Profiling Application: cubie_light_load
================================================================================

Establishing baseline (5.0s)...
Establishing baseline for 5.0 seconds...
Baseline established:
  CPU: 26.9%
  Memory: 53462 MB (82.3%)
  GPU: 0 device(s)
  NPU: 0.0% (available: False)
  Disk I/O: 0.0 MB/s read, 4.6 MB/s write

✓ File validation passed: C:\Users\NickV\cubie-verification\pipeline\cubie_load_light.py

Launching: python pipeline/cubie_load_light.py 75
Launching application 'cubie_light_load': python pipeline/cubie_load_light.py 75

✗ Failed to launch application: Failed to launch application: module 'os' has no attribute 'setsid'


`os.setsid` is Linux-only. The `--app` launch mode won't work on Windows. Let me use `--pid` mode for all three. I'll start each load script in the background and profile by PID.


cd "C:\Users\NickV\cubie-verification" && python pipeline/cubie_load_light.py 90 &

Start light load in background


<task-notification>
<task-id>bor4ezu1c</task-id>
<tool-use-id>toolu_01F11TVcADVLQL47JZ19otRo</tool-use-id>
<output-file>C:\Users\NickV\AppData\Local\Temp\claude\C--Users-NickV\bfc2bfbf-6683-4826-bf18-edd758f725bb\tasks\bor4ezu1c.output</output-file>
<status>completed</status>
<summary>Background command "Start light load in background" completed (exit code 0)</summary>
</task-notification>


Command running in background with ID: bor4ezu1c. Output is being written to: C:\Users\NickV\AppData\Local\Temp\claude\C--Users-NickV\bfc2bfbf-6683-4826-bf18-edd758f725bb\tasks\bor4ezu1c.output


sleep 3 && cat "C:\Users\NickV\AppData\Local\Temp\claude\C--Users-NickV\bfc2bfbf-6683-4826-bf18-edd758f725bb\tasks\bor4ezu1c.output" 2>&1

Get light load PID


PID: 58936


cd "C:\Users\NickV\cubie-verification" && PYTHONIOENCODING=utf-8 python "edge-ai-sizing-tool/workers/custom-application-profiling/main.py" profile --pid 58936 --name "cubie_light_load" --duration 60 --baseline 5 --output "artifacts/intel_profile_light.json" 2>&1

Intel profiler: light load (1 req/sec)


use a docker container


╔══════════════════════════════════════════════════════════════════════════════╗
║                    PLATFORM PROFILER v1.0                                    ║
║               CPU/GPU/NPU/Memory Utilization Analysis                        ║
╚══════════════════════════════════════════════════════════════════════════════╝

Profiling Application: cubie_light_load
================================================================================

Establishing baseline (5.0s)...
Establishing baseline for 5.0 seconds...
Baseline established:
  CPU: 24.9%
  Memory: 53337 MB (82.1%)
  GPU: 0 device(s)
  NPU: 0.0% (available: False)
  Disk I/O: 0.0 MB/s read, 4.6 MB/s write

Attaching to PID: 58936
Attaching to process 58936: C:\Python314\python.exe pipeline/cubie_load_light.py 90
Attached to process tree with 1 process(es):
  Root PID: 58936
Monitoring application 'cubie_light_load' for 60.0 seconds...
  Process tree tracking: enabled

Monitoring completed:
  Process Tree Summary:
    Root PID: 58936
    Final process count: 1
    Peak process count: 1
    Total spawns: 0
    Total exits: 0

  Resource Utilization:
    CPU (24 cores): 0.0% (avg), 0.0% (peak)
    [Total: 0.0% avg, 0.0% peak across all cores]
  Memory: 16 MB (avg), 16 MB (peak)
  GPU Compute: 0.0% (avg), 0.0% (peak)
  GPU Memory: 0 MB (avg), 0 MB (peak)
  NPU: 0.0% (avg), 0.0% (peak)
  Disk I/O: 0.1/0.3 MB/s (avg read/write)
    [Peak: 3.2/12.5 MB/s]
Note: Process 58936 is still running (attached mode)

================================================================================
PROFILING RESULTS
================================================================================

Application: cubie_light_load
Duration: 60.6s

Resource             Average              Peak
--------------------------------------------------------------------------------
CPU (normalized)        0.0%                  0.0%
  ({profile.logical_cores} cores) [  0.0% total] [           0.0% total]
Memory                       16 MB              16 MB
GPU Compute             0.0%                  0.0%
GPU Memory                    0 MB               0 MB
NPU                     0.0%                  0.0%
Disk Read                 0.1 MB/s              3.2 MB/s
Disk Write                0.3 MB/s             12.5 MB/s

================================================================================
BOTTLENECK ANALYSIS
================================================================================

Workload Type: light_workload
Overall Health: HEALTHY
Summary: Workload type: light_workload. No significant bottlenecks detected.

Detected Bottlenecks:
--------------------------------------------------------------------------------

✓ Profile saved to: artifacts/intel_profile_light.json


The user wants me to use a Docker container to run the Intel Edge AI Sizing Tool. Let me pivot — instead of running the CLI profiler directly on Windows (which has limitations like missing `os.setsid` and no GPU/NPU monitoring), I'll set up the full sizing tool in a Docker container.

Let me check what Docker images or compose files are available in the sizing tool repo.


No files found


No Docker files in the sizing tool repo. Let me check if there's a deployment guide that mentions containers.


edge-ai-sizing-tool\DEPLOYMENT.md
edge-ai-sizing-tool\DEVELOPMENT.md


Check Docker status


CONTAINER ID   IMAGE                                 COMMAND                  CREATED        STATUS                  PORTS                                                                                NAMES
4f4df1ae7c6a   e8cbb5c3ae86                          "docker-entrypoint.s…"   27 hours ago   Up 27 hours                                                                                elated_bell
aae60c08a2bb   e8cbb5c3ae86                          "docker-entrypoint.s…"   27 hours ago   Up 27 hours                                                                                inspiring_lewin
20c150d2490b   e8cbb5c3ae86                          "docker-entrypoint.s…"   32 hours ago   Up 32 hours                                                                                condescending_chatterjee
74db538078a4   e8cbb5c3ae86                          "docker-entrypoint.s…"   2 days ago     Up 2 days                                                                                brave_spence


Docker is running. Let me read the deployment doc to see if there's a container option, and also check the development doc.


1	# Deployment
2
3	This section guides you through preparing and deploying your application on different target system. Follow these steps carefully to create a reliable deployment package and execute the application efficiently.
4
5	## 1. Setup Platform
6
7	Refer to the [Edge Developer Kit Reference Scripts](https://github.com/intel/edge-developer-kit-reference-scripts) for detailed instructions on preparing your system.
8
9	## 2. Install Dependencies
10
11	Run the provided `install.sh` script to install all necessary dependencies:
12
13	```bash
14	./install.sh
15	```
16
17	## 3. Setup Environment
18
19	Configure the environment details securely based on the `frontend/.env.example`
20
21	## 4. Generate Distribution Package
22
23	Navigate to the `frontend` directory and execute the following command to create a distribution package:
24
25	```bash
26	npm run dist
27	```
28
29	This command will generate an archive `.zip` file of the package in the `../dist` folder, such as `../dist/edge-ai-sizing-tool_<version>.zip`. The version number will depend on the current version specified in your package configuration.
30
31	## 5. Transfer and Extract Package
32
33	Transfer the generated `.zip` file to the target system where you intend to deploy the application. Once transferred, extract the contents of the `.zip` file.
34
35	## 6. Deploy and Run the Application
36
37	Follow the instructions for your operating system:
38
39	### For Linux:
40
41	Navigate to the extracted folder and execute the following commands to install and launch the application:
42
43	```bash
44	cd [extracted_folder]
45	./install.sh
46	./edge-ai-sizing-tool.sh
47	```
48
49	### For Windows:
50
51	Navigate to the extracted folder and double-click `install.cmd` to set up the application. Once installation is complete, double-click `edge-ai-sizing-tool.cmd` to start the application.
52
53	## 7. Optional: Uninstall the Application
54
55	If you need to uninstall the application, you can use the provided uninstall scripts:
56
57	### For Linux:
58
59	```bash
60	cd [extracted_folder]
61	./uninstall.sh
62	```
63
64	### For Windows:
65
66	Navigate to the extracted folder and double-click `uninstall.cmd` to remove the application.

# Deployment

This section guides you through preparing and deploying your application on different target system. Follow these steps carefully to create a reliable deployment package and execute the application efficiently.

## 1. Setup Platform

Refer to the [Edge Developer Kit Reference Scripts](https://github.com/intel/edge-developer-kit-reference-scripts) for detailed instructions on preparing your system.

## 2. Install Dependencies

Run the provided `install.sh` script to install all necessary dependencies:

```bash
./install.sh
```

## 3. Setup Environment

Configure the environment details securely based on the `frontend/.env.example`

## 4. Generate Distribution Package

Navigate to the `frontend` directory and execute the following command to create a distribution package:

```bash
npm run dist
```

This command will generate an archive `.zip` file of the package in the `../dist` folder, such as `../dist/edge-ai-sizing-tool_<version>.zip`. The version number will depend on the current version specified in your package configuration.

## 5. Transfer and Extract Package

Transfer the generated `.zip` file to the target system where you intend to deploy the application. Once transferred, extract the contents of the `.zip` file.

## 6. Deploy and Run the Application

Follow the instructions for your operating system:

### For Linux:

Navigate to the extracted folder and execute the following commands to install and launch the application:

```bash
cd [extracted_folder]
./install.sh
./edge-ai-sizing-tool.sh
```

### For Windows:

Navigate to the extracted folder and double-click `install.cmd` to set up the application. Once installation is complete, double-click `edge-ai-sizing-tool.cmd` to start the application.

## 7. Optional: Uninstall the Application

If you need to uninstall the application, you can use the provided uninstall scripts:

### For Linux:

```bash
cd [extracted_folder]
./uninstall.sh
```

### For Windows:

Navigate to the extracted folder and double-click `uninstall.cmd` to remove the application.


No Docker support in the sizing tool itself. The approach: I'll create a Docker container that runs the Intel profiler + Cubie load test together on Ubuntu (the profiler's native OS), which gives us the full profiling experience including proper process management.

Let me build a container that:
1. Installs Rust + Python + profiler deps
2. Builds the Cubie FFI .so
3. Runs the load test + Intel profiler inside the container


List profiler directory contents


__pycache__
bottleneck_analyzer.py
main.py
requirements.txt


1	#!/usr/bin/env python3
2
3	# Copyright (C) 2025 Intel Corporation
4	# SPDX-License-Identifier: Apache-2.0
5
6	"""
7	Bottleneck Analyzer - Performance Bottleneck Detection
8	=======================================================
9
10	Analyzes application profiling data to automatically detect and diagnose
11	performance bottlenecks across CPU, Memory, GPU, NPU, and Thermal resources.
12
13	Features:
14	- Automatic bottleneck detection with severity classification
15	- Intelligent workload type classification
16	- Actionable optimization recommendations
17
18	Usage:
19	    from bottleneck_analyzer import BottleneckAnalyzer
20
21	    analyzer = BottleneckAnalyzer()
22	    analysis = analyzer.analyze(profile, baseline)
23	"""
24
25	from typing import Dict, List, Optional, Any
26	from dataclasses import dataclass, field
27
28
29	# ============================================================================
30	# Data Models

#!/usr/bin/env python3

# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

"""
Bottleneck Analyzer - Performance Bottleneck Detection
=======================================================

Analyzes application profiling data to automatically detect and diagnose
performance bottlenecks across CPU, Memory, GPU, NPU, and Thermal resources.

Features:
- Automatic bottleneck detection with severity classification
- Intelligent workload type classification
- Actionable optimization recommendations

Usage:
    from bottleneck_analyzer import BottleneckAnalyzer

    analyzer = BottleneckAnalyzer()
    analysis = analyzer.analyze(profile, baseline)
"""

from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field

# ============================================================================
# Data Models


Good. Now let me create the Dockerfile and a run script.


Write

FROM ubuntu:24.04

# Prevent interactive prompts
ENV DEBIAN_FRONTEND=noninteractive

# Install system deps
RUN apt-get update && apt-get install -y \
    curl \
    build-essential \
    python3 \
    python3-pip \
    python3-venv \
    pkg-config \
    && rm -rf /var/lib/apt/lists/*

# Install Rust
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
ENV PATH="/root/.cargo/bin:${PATH}"

WORKDIR /workspace

# Copy Rust source and build FFI
COPY Cargo.toml ./
COPY bare-metal/ bare-metal/
COPY bare-metal-tests/ bare-metal-tests/
COPY cubie-ffi/ cubie-ffi/

# Build the FFI shared library
RUN cargo build --release --manifest-path cubie-ffi/Cargo.toml

# Copy pipeline code
COPY pipeline/ pipeline/

# Copy Intel profiler
COPY edge-ai-sizing-tool/workers/custom-application-profiling/main.py /profiler/main.py
COPY edge-ai-sizing-tool/workers/custom-application-profiling/bottleneck_analyzer.py /profiler/bottleneck_analyzer.py
COPY edge-ai-sizing-tool/workers/custom-application-profiling/requirements.txt /profiler/requirements.txt

# Install Python deps
RUN python3 -m pip install --break-system-packages psutil fastapi uvicorn

# Create output directory
RUN mkdir -p /output

# Copy the profiling runner script
COPY run_intel_profiler.sh /workspace/run_intel_profiler.sh
RUN chmod +x /workspace/run_intel_profiler.sh

ENTRYPOINT ["/workspace/run_intel_profiler.sh"]


File created successfully at: C:\Users\NickV\cubie-verification\Dockerfile.intel-profiler

FROM ubuntu:24.04

# Prevent interactive prompts
ENV DEBIAN_FRONTEND=noninteractive

# Install system deps
RUN apt-get update && apt-get install -y \
    curl \
    build-essential \
    python3 \
    python3-pip \
    python3-venv \
    pkg-config \
    && rm -rf /var/lib/apt/lists/*

# Install Rust
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
ENV PATH="/root/.cargo/bin:${PATH}"

WORKDIR /workspace

# Copy Rust source and build FFI
COPY Cargo.toml ./
COPY bare-metal/ bare-metal/
COPY bare-metal-tests/ bare-metal-tests/
COPY cubie-ffi/ cubie-ffi/

# Build the FFI shared library
RUN cargo build --release --manifest-path cubie-ffi/Cargo.toml

# Copy pipeline code
COPY pipeline/ pipeline/

# Copy Intel profiler
COPY edge-ai-sizing-tool/workers/custom-application-profiling/main.py /profiler/main.py
COPY edge-ai-sizing-tool/workers/custom-application-profiling/bottleneck_analyzer.py /profiler/bottleneck_analyzer.py
COPY edge-ai-sizing-tool/workers/custom-application-profiling/requirements.txt /profiler/requirements.txt

# Install Python deps
RUN python3 -m pip install --break-system-packages psutil fastapi uvicorn

# Create output directory
RUN mkdir -p /output

# Copy the profiling runner script
COPY run_intel_profiler.sh /workspace/run_intel_profiler.sh
RUN chmod +x /workspace/run_intel_profiler.sh

ENTRYPOINT ["/workspace/run_intel_profiler.sh"]


#!/bin/bash
# Intel Edge AI Sizing Tool - Custom Application Profiler
# Profiles Cubie Trust Compiler at 3 load levels (light, medium, heavy)

set -e

echo "=============================================="
echo "Intel Edge AI Sizing Tool - Platform Profiler"
echo "Cubie Trust Compiler Profiling Session"
echo "=============================================="
echo ""

# Verify the FFI library exists
if [ ! -f /workspace/target/release/libcubie_ffi.so ]; then
    echo "ERROR: libcubie_ffi.so not found!"
    exit 1
fi

echo "FFI library: $(ls -la /workspace/target/release/libcubie_ffi.so)"
echo ""

# Quick sanity check
echo "=== Sanity Check ==="
python3 -c "
import sys
sys.path.insert(0, '/workspace')
from pipeline.cubie_gate import CubieGate
g = CubieGate()
req = g.create_test_request()
result = g.admit(req)
print(f'cubie_admit() = {result}')
bench = g.benchmark(100000)
print(f'Benchmark: {bench}')
"
echo ""

# Function to profile at a given load level
run_profile() {
    local level=$1
    local script=$2
    local duration=$3
    local output_file=$4

    echo "=== Profile: $level load ==="
    echo "Starting load generator: $script"

    # Start load generator in background
    python3 /workspace/pipeline/$script $((duration + 20)) &
    local load_pid=$!

    # Wait for process to start
    sleep 3

    echo "Load generator PID: $load_pid"
    echo "Running Intel profiler for ${duration}s..."

    # Run Intel profiler
    cd /profiler
    python3 main.py profile \
        --pid $load_pid \
        --name "cubie_${level}" \
        --duration $duration \
        --baseline 5 \
        --output "/output/$output_file"

    echo ""
    echo "Profile saved to /output/$output_file"

    # Kill load generator if still running
    kill $load_pid 2>/dev/null || true
    wait $load_pid 2>/dev/null || true

    echo "=== $level profile complete ==="
    echo ""
    sleep 2
}

# Also run status check first
echo "=== Platform Status ==="
cd /profiler
python3 main.py status
echo ""

# Run all 3 load levels
run_profile "light"  "cubie_load_light.py"         60  "intel_profile_light.json"
run_profile "medium" "cubie_load_medium.py"         60  "intel_profile_medium.json"
run_profile "heavy"  "cubie_load_for_profiler.py"   60  "intel_profile_heavy.json"

echo "=============================================="
echo "All profiles complete!"
echo "Output files:"
ls -la /output/
echo "=============================================="


File created successfully at: C:\Users\NickV\cubie-verification\run_intel_profiler.sh

#!/bin/bash
# Intel Edge AI Sizing Tool - Custom Application Profiler
# Profiles Cubie Trust Compiler at 3 load levels (light, medium, heavy)

set -e

echo "=============================================="
echo "Intel Edge AI Sizing Tool - Platform Profiler"
echo "Cubie Trust Compiler Profiling Session"
echo "=============================================="
echo ""

# Verify the FFI library exists
if [ ! -f /workspace/target/release/libcubie_ffi.so ]; then
    echo "ERROR: libcubie_ffi.so not found!"
    exit 1
fi

echo "FFI library: $(ls -la /workspace/target/release/libcubie_ffi.so)"
echo ""

# Quick sanity check
echo "=== Sanity Check ==="
python3 -c "
import sys
sys.path.insert(0, '/workspace')
from pipeline.cubie_gate import CubieGate
g = CubieGate()
req = g.create_test_request()
result = g.admit(req)
print(f'cubie_admit() = {result}')
bench = g.benchmark(100000)
print(f'Benchmark: {bench}')
"
echo ""

# Function to profile at a given load level
run_profile() {
    local level=$1
    local script=$2
    local duration=$3
    local output_file=$4

    echo "=== Profile: $level load ==="
    echo "Starting load generator: $script"

    # Start load generator in background
    python3 /workspace/pipeline/$script $((duration + 20)) &
    local load_pid=$!

    # Wait for process to start
    sleep 3

    echo "Load generator PID: $load_pid"
    echo "Running Intel profiler for ${duration}s..."

    # Run Intel profiler
    cd /profiler
    python3 main.py profile \
        --pid $load_pid \
        --name "cubie_${level}" \
        --duration $duration \
        --baseline 5 \
        --output "/output/$output_file"

    echo ""
    echo "Profile saved to /output/$output_file"

    # Kill load generator if still running
    kill $load_pid 2>/dev/null || true
    wait $load_pid 2>/dev/null || true

    echo "=== $level profile complete ==="
    echo ""
    sleep 2
}

# Also run status check first
echo "=== Platform Status ==="
cd /profiler
python3 main.py status
echo ""

# Run all 3 load levels
run_profile "light"  "cubie_load_light.py"         60  "intel_profile_light.json"
run_profile "medium" "cubie_load_medium.py"         60  "intel_profile_medium.json"
run_profile "heavy"  "cubie_load_for_profiler.py"   60  "intel_profile_heavy.json"

echo "=============================================="
echo "All profiles complete!"
echo "Output files:"
ls -la /output/
echo "=============================================="


Now build and run the container.


Bash

cd "C:\Users\NickV\cubie-verification" && docker build -f Dockerfile.intel-profiler -t cubie-intel-profiler . 2>&1

Build Docker container for Intel profiling