mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-06-03 00:15:32 -05:00
f1d47330b3
- Remove unnecessary module_paths.txt file for cleaner architecture - Update export command to discover modules dynamically from modules/source/ - Simplify nbdev command to support --all and module-specific exports - Use single source of truth: nbdev settings.ini for module paths - Clean up import structure in setup module for proper nbdev export - Maintain clean separation between module discovery and export logic This implements a proper software engineering approach with: - Single source of truth (settings.ini) - Dynamic discovery (no hardcoded paths) - Clean CLI interface (tito package nbdev --export [--all|module]) - Robust error handling with helpful feedback
100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/00_setup/setup_dev.ipynb.
|
|
|
|
# %% auto 0
|
|
__all__ = ['personal_info', 'system_info']
|
|
|
|
# %% ../../modules/source/00_setup/setup_dev.ipynb 4
|
|
def personal_info() -> Dict[str, str]:
|
|
"""
|
|
Return personal information for this TinyTorch installation.
|
|
|
|
TODO: Implement personal information configuration.
|
|
|
|
STEP-BY-STEP:
|
|
1. Create a dictionary with your personal details
|
|
2. Include: developer (your name), email, institution, system_name, version
|
|
3. Use your actual information (not placeholder text)
|
|
4. Make system_name unique and descriptive
|
|
5. Keep version as '1.0.0' for now
|
|
|
|
EXAMPLE:
|
|
{
|
|
'developer': 'Vijay Janapa Reddi',
|
|
'email': 'vj@eecs.harvard.edu',
|
|
'institution': 'Harvard University',
|
|
'system_name': 'VJ-TinyTorch-Dev',
|
|
'version': '1.0.0'
|
|
}
|
|
|
|
HINTS:
|
|
- Replace the example with your real information
|
|
- Use a descriptive system_name (e.g., 'YourName-TinyTorch-Dev')
|
|
- Keep email format valid (contains @ and domain)
|
|
- Make sure all values are strings
|
|
"""
|
|
### BEGIN SOLUTION
|
|
return {
|
|
'developer': 'Vijay Janapa Reddi',
|
|
'email': 'vj@eecs.harvard.edu',
|
|
'institution': 'Harvard University',
|
|
'system_name': 'VJ-TinyTorch-Dev',
|
|
'version': '1.0.0'
|
|
}
|
|
### END SOLUTION
|
|
|
|
# %% ../../modules/source/00_setup/setup_dev.ipynb 6
|
|
def system_info() -> Dict[str, Any]:
|
|
"""
|
|
Query and return system information for this TinyTorch installation.
|
|
|
|
TODO: Implement system information queries.
|
|
|
|
STEP-BY-STEP:
|
|
1. Get Python version using sys.version_info
|
|
2. Get platform using platform.system()
|
|
3. Get architecture using platform.machine()
|
|
4. Get CPU count using psutil.cpu_count()
|
|
5. Get memory using psutil.virtual_memory().total
|
|
6. Convert memory from bytes to GB (divide by 1024^3)
|
|
7. Return all information in a dictionary
|
|
|
|
EXAMPLE OUTPUT:
|
|
{
|
|
'python_version': '3.9.7',
|
|
'platform': 'Darwin',
|
|
'architecture': 'arm64',
|
|
'cpu_count': 8,
|
|
'memory_gb': 16.0
|
|
}
|
|
|
|
HINTS:
|
|
- Use f-string formatting for Python version: f"{major}.{minor}.{micro}"
|
|
- Memory conversion: bytes / (1024^3) = GB
|
|
- Round memory to 1 decimal place for readability
|
|
- Make sure data types are correct (strings for text, int for cpu_count, float for memory_gb)
|
|
"""
|
|
### BEGIN SOLUTION
|
|
# Get Python version
|
|
version_info = sys.version_info
|
|
python_version = f"{version_info.major}.{version_info.minor}.{version_info.micro}"
|
|
|
|
# Get platform information
|
|
platform_name = platform.system()
|
|
architecture = platform.machine()
|
|
|
|
# Get CPU information
|
|
cpu_count = psutil.cpu_count()
|
|
|
|
# Get memory information (convert bytes to GB)
|
|
memory_bytes = psutil.virtual_memory().total
|
|
memory_gb = round(memory_bytes / (1024**3), 1)
|
|
|
|
return {
|
|
'python_version': python_version,
|
|
'platform': platform_name,
|
|
'architecture': architecture,
|
|
'cpu_count': cpu_count,
|
|
'memory_gb': memory_gb
|
|
}
|
|
### END SOLUTION
|