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H5py Alternatives
Similar projects and alternatives to h5py
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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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Apache Arrow
Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
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viztracer
VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
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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.
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better h5py alternative or higher similarity.
h5py reviews and mentions
Posts with mentions or reviews of h5py.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-09-26.
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Working with data files too large for RAM
There's some good answers here, but another option I haven't seen suggested: Convert your txt file to HDF5 (Regardless if you follow my approach here, you should really consider converting your data to anything but a txt file). There's a nice library for working with it in python called h5py. The HDF format is designed specifically with working with very large sets of data (it even has compression options), often scientific in nature, but it's not a database. As far as how this fixes the specific issue you you've described, you can utilize numpy slicing to load one chunk your data at a time. Here's a stackoverflow answer which discusses a solution.
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How to combine multiple numpy arrays stored on disk which are too big to fit in RAM?
If it is a dataset, it should consist of individual instances. You could store these instances in separate files. Otherwise, HDF5 is a very convenient storage format. It allows random read/write access to elements of arrays stored on disk and has excellent Python support in form of the h5py package.
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Is Python really this slow?
If possible, try to monitor your memory usage during execution and if you see that you are consistently exceeding ~50% (my own rule of thumb, though you may want to discuss this with others as well) of what's available. If you are consistently using most of the available memory, then it's likely worth taking a moment to evaluate whether you can operate on subsets of the data from start to finish, and leave the rest of the data on disk until you are almost ready to use it. Tools like h5py are very helpful in these kinds of situations.
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Python packages as API end points.
Yea - I really struggled with getting the correct version on h5py to work with both tensorflow and allenai nlp modules. May be its about finding the right version of libraries. Github Issue. I ended up using pickle to save stuff, like John who commented on 26/03/2020 on the same(closed) issue.
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Tracing and visualizing the Python GIL with perf and VizTracer
Apply these to more issues, like in https://github.com/h5py/h5py/issues/1516
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A note from our sponsor - InfluxDB
www.influxdata.com | 24 Apr 2024
Stats
Basic h5py repo stats
5
1,996
8.6
9 days ago
h5py/h5py is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.
The primary programming language of h5py is Python.
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