Bibliographic record
Abstract
Stephane Wirtel - Exploring our Python Interpreter During the last CPython sprints at PyCon US (Montreal), I started to contribute to the CPython project and I wanted to understand the beast. In this case, there is only one solution, trace the code from the beginning. From the command line to the interpreter, we will take part to an adventure. The idea behind is just to show how CPython works for a new contributor. ----- During my last CPython sprint, I started to contribute to the CPython code and I wanted to understand the beast. In this case, there is only one solution, trace the code from the beginning. From the command line to the interpreter, we will take part to an adventure * Overview of the structure of the project and the directories. * From the Py_Main function to the interpreter. * The used technics for the Lexer, Parser and the generation of the AST and of course of the Bytecodes. * We will see some bytecodes with the dis module. * How does VM works, it's a stack machine. * The interpreter and its main loop of the Virtual Machine. The idea behind is just to show how CPython works for a new contributor to CPython. From the command line, we will learn that Python is a library and that we can embed it in a C project. In fact we will see the Py_Main function to the ceval.c file of the interpreter. But there is no magic in the CPython code, we will travel in the lexer and the parser of CPython, and why not, by the AST for one Python expression. After the AST, we will visit the Compiler and the Bytecodes for the interpreter. Of course, we will learn there is the peepholer where some basic instructions are optimised by the this component. And of course, the interpreter, this virtual machine is really interesting for the newbiew, because it's a big stack where the bytecodes are executed one by one on the stack and the ceval.c file.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.245 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".