Observed since 20 Oct, 17:55 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
A library for benchmarking the tflops of accelerators
pip install -U accelrod
[all] includes nbformat to visualize the result in notebook
pip install -U accelrod[all]
import torch
from accelrod.benchmark import benchrun, plot_result
# without max_dimension, the matrix size will be determined based on available VRAM
result = benchrun(
algorithm="GEMM",
device="auto",
as_dataframe="pandas",
params={
# "max_dimension": 2048,
"dtype": [torch.float32, torch.float16, torch.bfloat16],
},
)
# plot in notebook
import plotly.offline as pyo
pyo.init_notebook_mode()
plot_result(result)
Sync the project's dependencies with the environment.
uv sync
lock dependencies declared in a pyproject.toml
uv pip compile pyproject.toml -o requirements.txt
uv add --dev ruff pytest
uv pip install -e .[all,dev]
uv pip install dist/accelrod-0.1.0-py3-none-any.whl uv run --with accelrod --no-project --refresh-package accelrod -- python -c "import accelrod"
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.1.0 | 12 Feb 2025 | 5 |
This describes our collected data; it is not a security assessment.
Observed since 20 Oct, 17:55 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:1a2fdaf52c9c089481ab7765e6e62aa36a7246c20cc5bd699e5ee4e16235f9c1