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Cm Python Packages

Python packages with the GitHub topic cm. Sorted by monthly downloads, with stars and forks.
mlcommons
ck

Collective Knowledge (CK), Collective Mind (CM/CMX) and MLPerf automations: community-driven projects to learn how to run AI, ML, and other emerging workloads more efficiently and cost-effectively across diverse models, datasets, software, and hardware using MLPerf methodology and benchmarks.

3K 649 122
mlcommons
cmind

Collective Knowledge (CK), Collective Mind (CM/CMX) and MLPerf automations: community-driven projects to learn how to run AI, ML, and other emerging workloads more efficiently and cost-effectively across diverse models, datasets, software, and hardware using MLPerf methodology and benchmarks.

2K 649 122
pyted
pbinance

Python Binance API interface

276 67 15
mlcommons
cmx4mlops

Collective Knowledge (CK), Collective Mind (CM/CMX) and MLPerf automations: community-driven projects to learn how to run AI, ML, and other emerging workloads more efficiently and cost-effectively across diverse models, datasets, software, and hardware using MLPerf methodology and benchmarks.

156 649 122
marcostulliosouza
pycmcontrol-mqtt

Biblioteca Python para integração com o Driver Dispositivo CmControl v1.00 via MQTT

77 0 0
mlcommons
cmx4mlperf

Collective Knowledge (CK), Collective Mind (CM/CMX) and MLPerf automations: community-driven projects to learn how to run AI, ML, and other emerging workloads more efficiently and cost-effectively across diverse models, datasets, software, and hardware using MLPerf methodology and benchmarks.

77 649 122
ctuning
ckdb

Collective Knowledge (CK), Collective Mind (CM/CMX) and MLPerf automations: community-driven projects to learn how to run AI, ML, and other emerging workloads more efficiently and cost-effectively across diverse models, datasets, software, and hardware using MLPerf methodology and benchmarks.

49 649 122
ctuning
mldb

Collective Knowledge (CK), Collective Mind (CM/CMX) and MLPerf automations: community-driven projects to learn how to run AI, ML, and other emerging workloads more efficiently and cost-effectively across diverse models, datasets, software, and hardware using MLPerf methodology and benchmarks.

36 649 122
    • Original data from PyPI, GitHub, and OSV; downloads served through ClickHouse
    • Sources & methodology