Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

DeepCL Python wrappers

Python wrapper for DeepCL

Pre-requisites

  • You must have first installed and activated DeepCL native libraries, see Build.md

  • numpy

To install from pip

pip install --upgrade DeepCL

How to use

See test_deepcl.py for an example of:

  • creating a network, with several layers

  • loading mnist data

  • training the network using a higher-level interface (NetLearner)

For examples of using lower-level entrypoints, see test_lowlevel.py:

  • creating layers directly

  • running epochs and forward/backprop directly

For example of using q-learning, see test_qlearning.py.

To install from source

Pre-requisites:

  • on Windows:

  • Python 2.7 or Python 3.4

  • A compiler:

  • on linux:

  • Python 2.7 or Python 3.4

  • g++, supporting c++0x, eg 4.4 or higher

  • have first already built the native libraries, see Build.md

  • have activated the native library installation, ie called dist/bin/activate.sh, or dist/bin/activate.bat

  • numpy installed

To install:

cd python
python setup.py install

Changes

  • 30 July 2016:

  • Added net.getNetdef(). Note that this is only an approximate representation of the network

  • 29 July 2016:

  • New feature: can provide image tensor as 4d tensor now ,instead of 1d tensor (1d tensor ok too)

  • CHANGE: all image and label tensors must be provided as numpy tensors now, array.array no longer valid input

  • bug fix: qlearning works again :-)

  • 25 July 2016:

  • added RandomSingleton class, to set the seed for weights initialization

  • added xor.py example

Metadata

Release files for DeepCL 10.2.0alpha3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for DeepCL 10.2.0alpha3
File Size Uploaded Provenance
DeepCL-10.2.0alpha3.tar.gz 156.3 kB Without provenance Details

Built distributions (wheels)

Table of built distributions (wheels) for DeepCL 10.2.0alpha3
File Interpreter ABI Platform
DeepCL-10.2.0alpha3-py3.4-linux-x86_64.egg Legacy Egg format - - Details
DeepCL-10.2.0alpha3-py2.7-linux-x86_64.egg Legacy Egg format - - Details

Total release size: 1.3 MB

Release files / DeepCL-10.2.0alpha3.tar.gz

Download URL DeepCL-10.2.0alpha3.tar.gz
Size 156.3 kB
Tags Source
SHA-256 checksum
How to use checksums
9343e559f7a8fb844b27a6fe4849b0004ce556327e81d65f82056903195e98b0
BLAKE2b-256 checksum
How to use checksums
93ded8a12a1baaace5513582435353b4992bb9ed7eaca6a964d77e8057c1d8c8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / DeepCL-10.2.0alpha3-py3.4-linux-x86_64.egg

Download URL DeepCL-10.2.0alpha3-py3.4-linux-x86_64.egg
Size 582.9 kB
Tags Egg
SHA-256 checksum
How to use checksums
d007c735f146342bedf608569307b9858fa3f4c702ad1e62f219d4775e62e3ad
BLAKE2b-256 checksum
How to use checksums
509b7ec7b6acfb9313ddf8f1313a7c202bd9ba3670c0441062b9c85b464e671f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / DeepCL-10.2.0alpha3-py2.7-linux-x86_64.egg

Download URL DeepCL-10.2.0alpha3-py2.7-linux-x86_64.egg
Size 543.3 kB
Tags Egg
SHA-256 checksum
How to use checksums
ea73e38f5e1ffa982ed95a402f86754ec57991122745c5040c0eb1982568c52b
BLAKE2b-256 checksum
How to use checksums
1026818387a12466526f8cd6a31b8dda1c4d67e8b60a11e73105499edf879920
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Security for DeepCL 10.2.0alpha3

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Release history Release notifications | RSS feed

This release
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page