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Numexpr Alternatives
Similar projects and alternatives to numexpr
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Apache Arrow
Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
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scalene
Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
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pytorch-lightning
Discontinued Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning] (by PyTorchLightning)
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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greptimedb
An open-source, cloud-native, distributed time-series database with PromQL/SQL/Python supported. Available on GreptimeCloud.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better numexpr alternative or higher similarity.
numexpr reviews and mentions
Posts with mentions or reviews of numexpr.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-29.
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Making Python 100x faster with less than 100 lines of Rust
You can just slap numexpr on top of it to compile this line on the fly.
https://github.com/pydata/numexpr
- Extending Python with Rust
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[D] How to avoid CPU bottlenecking in PyTorch - training slowed by augmentations and data loading?
Are you doing any costly chained NumPy operations in your preprocessing? E.g. max(abs(large_ary)), this produces multiple copies of your data, https://github.com/pydata/numexpr can greatly reduce time spent with such operations
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Selection in pandas using query
What is not entirely obvious here is that under the hood you can install a nice library called numexpr (docs, src) that exists to make calculations with large NumPy (and pandas) objects potentially much faster. When you use query or eval, this expression is passed into numexpr and optimized using its bag of tricks. Expected performance improvement can be between .95x and up to 20x, with average performance around 3-4x for typical use cases. You can read details in the docs, but essentially numexpr takes vectorized operations and makes them work in chunks that optimize for cache and CPU branch prediction. If your arrays are really large, your cache will not be hit as often. If you break your large arrays into very small pieces, your CPU won’t be as efficient.
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A note from our sponsor - WorkOS
workos.com | 26 Apr 2024
Stats
Basic numexpr repo stats
4
2,140
8.2
25 days ago
pydata/numexpr is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of numexpr is Python.
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