https://github.com/nikhillingadhal1999/aipymemtimeprofiler
AIPyMemTimeProfiler gives you powerful insights into your Python code,no decorators, no setup, no hassle. Instantly track memory usage and execution time for every function in your app. Just run your script and get a clear, JSON-based performance report tailored to your codebase. Zero Code Changes. Maximum Visibility. Ideal for performance tuning.
https://github.com/nikhillingadhal1999/aipymemtimeprofiler
ai ai-enabled enabled flask flask-api flask-application llm memory memory-profiler memory-profiling ollama pip profile profiler profiling python python3 time time-profile time-profiling
Last synced: 3 days ago
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AIPyMemTimeProfiler gives you powerful insights into your Python code,no decorators, no setup, no hassle. Instantly track memory usage and execution time for every function in your app. Just run your script and get a clear, JSON-based performance report tailored to your codebase. Zero Code Changes. Maximum Visibility. Ideal for performance tuning.
- Host: GitHub
- URL: https://github.com/nikhillingadhal1999/aipymemtimeprofiler
- Owner: nikhillingadhal1999
- Created: 2025-04-10T18:57:44.000Z (16 days ago)
- Default Branch: main
- Last Pushed: 2025-04-23T15:13:05.000Z (3 days ago)
- Last Synced: 2025-04-23T16:17:00.843Z (3 days ago)
- Topics: ai, ai-enabled, enabled, flask, flask-api, flask-application, llm, memory, memory-profiler, memory-profiling, ollama, pip, profile, profiler, profiling, python, python3, time, time-profile, time-profiling
- Language: Python
- Homepage:
- Size: 4.97 MB
- Stars: 17
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# Zero-Hassle AI Enabled Python Profiler for Time & Memory
AI enabled lightweight time and memory profiler for Python, zero-configuration python profiler that captures function-level performance metrics along with analysis of function implementation with AI Agent with **no code changes**. Ideal for:
- Python scripts
- Flask projects
- Real-time debugging of performance bottlenecks
---
## Features
### Zero Code Changes
Just run your Python script — no decorators, annotations, or modifications needed.
### Time & Memory Metrics
Automatically captures:
- Max execution time (CPU)
- Peak memory usage
- RSS memory growth
- Return object size
- Arguments passed
### Project-Aware Analysis
Only **your** code is profiled.
System libraries and external modules are ignored using intelligent project-path detection.
## Profiler Metrics
The following table describes the metrics collected by the profiler:
| **Metric** | **Description** | **Key** |
|--------------------------|---------------------------------------------------------------------------------------------------|-----------------------------------|
| **Function Name** | The name of the function being profiled. | `function` |
| **File Path** | The absolute path to the file where the function is defined. | `file` |
| **Line Number** | The line number where the function starts in the file. | `line` |
| **Execution Time** | Maximum time taken by each function in milliseconds (ms). | `max_time_ms` |
| **CPU Time** | Time spent by the CPU on this function (ms). | `cpu_time_ms` |
| **Peak Memory Usage** | Maximum memory usage during function execution in kilobytes (KB). | `max_mem` |
| **RSS Memory Growth** | Growth in Resident Set Size (RSS) memory in kilobytes (KB), helps spot memory leaks. | `mem_growth_rss_kb` |
| **Arguments** | The arguments passed to the function being profiled. | `args` |
| **Possible Memory Leak** | Indicates if a potential memory leak is detected (if any). | `possible_memory_leak` |
| **Notes** | Any additional notes related to the profiling data. | `note` |
| **Returned Object Size** | The size of the returned object in bytes. | `return_obj` |
Option to select a function for analysis, which is analysed by the Ollama model installed and configured.
This is the table providing the options for analysis.
### Available Functions for Analysis
| **Index** | **Function Name** | **File Path** |
|-----------|------------------------|----------------------------------|
| 0 | `function_one` | `/path/to/file_one.py` |
| 1 | `function_two` | `/path/to/file_two.py` |
| 2 | `function_three` | `/path/to/file_three.py` |
| 3 | `function_four` | `/path/to/file_four.py` |
| 4 | `function_five` | `/path/to/file_five.py` |
| ... | ... | ... |
| N | `function_n` | `/path/to/file_n.py` |
| N+1 | `Skip Analysis` | `-` |
### Structured JSON Reports
Each function includes:
- Function name
- Source file and line number
- Time (ms)
- Memory usage (KB)
- Return object size
- Arguments
- Memory growth & potential leaks
### Works with Any Project Structure
Handles **nested folder hierarchies** easily — just point to your project root and go.
---
## Setup Instructions
### 1. Set Environment Variables
```bash
export PROFILER_FILE_PATH="/absolute/path/to/your_script.py"
export PROFILER_DIR_PATH="/absolute/path/to/your/project/root"
```
If you just want to try, you can test it with sample_project.
> `export PROFILER_FILE_PATH="$(pwd)/sample_project/inside/app.py"`: The Python file to be profiled
> `export PROFILER_DIR_PATH="$(pwd)/sample_project/inside"`: Root of your project for accurate filtering
### 2. Optional: Suppress Console Output
By default, profiler prints a table to the console. To disable:
```bash
export CONSOLE_DISPLAY=False
```
## Environment Setup
If you **already have a virtual environment**, just install the dependencies:
```bash
make setup
```
This will install the requirements in your env
If you **don't have a virtual environment**, just install the dependencies:
```bash
make setup
```
This will create an env and install requirements
## LLM Environment Setup
Download Ollama from
[Ollama](https://ollama.com/)
```bash
ollama run
```
If you don't know which model to use.
> `ollama run deepseek-r1:1.5b`: It is preferable as it is light weight.
Set your model env variable.
```bash
export AGENT_NAME=""
export AGENTIC_PROFILER=True
```
```bash
make agent-setup
```
To use
## Run profiler
```bash
make run
```
This will:
- Read the env vars
- Launch your script
- Record memory + execution stats
- Save detailed JSON report
---
## Supported Use Cases
- Pure Python projects
- Flask APIs and apps
- Any directory layout
---
## Want More?
- [ ] Console table toggle
- [ ] HTML report output
- [ ] Jupyter Notebook integration
Pull requests are welcome!