SneppX Console

sneppx-console is the interactive, tab-autocompleted REPL for exploring the SNEPPX-Algo Python API without writing a script. It is built on the cmd2 library (via SneppX_ALG.interface_bindings.vizmon), provides history, syntax-highlighted output, and context-aware help for every command.

Starting the console

$env:PYTHONPATH = "bindings/python"
python -m SneppX_ALG.interface_bindings.sneppx_console

If the package is installed (pip install -e .), the entry point is on PATH:

sneppx-console

You should see:

SNEPPX-Algo Console  v1.1.1   (C backend: True)
Type help or ? for command list.
sneppx> █

The banner reports whether the C backend (_HAS_C_BACKEND) is loaded. If False, tensor ops still work via the NumPy fallback, but algorithm-stage methods (HSSModel.forward, Trainer.train_step, etc.) will raise RuntimeError: C backend not available.

Tab completion

Tab completion is available for commands, arguments, and symbols.

sneppx> from SneppX_ALG import Trans <TAB>
sneppx> # completes → Transformer  TransformerBlock
sneppx> load_model --name <TAB>
sneppx> # completes → llama-2-7b mistral-7b qwen2-7b deepseek-v2-lite

Trigger	Completes
Ctrl-Space or TAB at start of line	command name
TAB after from SneppX_ALG import	exported symbol names
TAB after --name on load_model	known model IDs
TAB after a function call ...	nothing (use dir() instead)

Commands

Command	Description
Tensor.zeros SHAPE	Create a zero tensor. Tensor.zeros 4 8 → shape (4,8).
Tensor.randn SHAPE [dtype] [cuda]	Random tensor.
run FILE [--backend cuda|cpu]	Execute a SneppX script / notebook cell file.
load_model --name llama-2-7b [--cache-dir DIR]	Fetch + load a model config (no weights without C backend).
serve --port 8000 [--host 127.0.0.1] [--auth-key K]	Start the FastAPI inference server in-process.
scan PATH --format c|hpp	Run sneppx-analyze security scan on a source path.
profile --duration 10 --output report.json	Record a 10-s profile to JSON.
checkpoint save PATH / checkpoint load PATH	Persist / restore trainer state.
distributed --world 4 --backend nccl	Print the launch env for a 4-rank job.
quantize --mode int4|int8|fp8 --model NAME	Quantize a loaded model in place.
list_backends	Show detected CUDA/NCCL/CPU feature flags.
exit / quit	Leave the console.

Built-in help

sneppx> help
sneppx> serve --help
sneppx> scan --help
sneppx> Tensor.zeros --help

Help text mirrors the docstring of the underlying binding, so it stays in sync with the code.

Example session

sneppx> Tensor.zeros 4 8
Tensor(shape=(4, 8), dtype=float32, device=cpu)

sneppx> from SneppX_ALG import Tensor, Linear, AdamW
sneppx> x = Tensor.randn 4 8
sneppx> lin = Linear(8, 16)
sneppx> y = lin.forward(x)
sneppx> y.shape
(4, 16)

sneppx> list_models
Available: llama-2-7b, llama-3-8b, mistral-7b, qwen2-7b, deepseek-v2-lite

sneppx> load_model --name mistral-7b
[SNEPPX from_pretrained] mistral-7b -> family=mistral, size=7B
  hidden_size=4096, layers=32, heads=32, kv_heads=8
config loaded (no weights — build C backend for inference)

sneppx> profile --duration 5 --output /tmp/prof.json
profiling 5.0s ... done
wrote /tmp/prof.json (32 entries)

sneppx> quantize --mode int4 --model mistral-7b
[quantize] INT4 sym  | weight=7.21 GB -> 3.61 GB (quantize_error snr=32.4 dB)

sneppx> exit
bye.

Scripting the console

Every command is a do_<name> method on the SneppX_ALG.interface_bindings.sneppx_console.SneppXConsole class, so you can subclass and override behavior — useful for CI smoke tests or custom workflows. Pass --file script.sneppx to run a batch of commands non- interactively (no TTY required).

Limitations

• The console is a thin wrapper; heavy compute still runs through the C backend. For headless use, prefer the CLI tools (sneppx-train, sneppx-serve, sneppx-quantize, sneppx-analyze) directly.
• CUDA tensors require a real GPU + NCCL; the console reports cuda_is_available() on startup so you can branch in scripts.