[Snyk] Security upgrade numpy from 1.21.3 to 1.22.2

[abdulrahman305]

This PR was automatically created by Snyk using the credentials of a real user.

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

• Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
  • gpt4all-training/requirements.txt

⚠️ Warning

scikit-learn 1.0.2 requires scipy, which is not installed.
nomic 3.0.15 requires pillow, which is not installed.
multiprocess 0.70.15 has requirement dill>=0.3.7, but you have dill 0.3.6.
matplotlib 3.5.3 requires pillow, which is not installed.

Vulnerabilities that will be fixed

By pinning:

Severity	Priority Score (*)	Issue	Upgrade	Breaking Change	Exploit Maturity
	506/1000 Why? Proof of Concept exploit, Has a fix available, CVSS 3.7	NULL Pointer Dereference SNYK-PYTHON-NUMPY-2321964	numpy: 1.21.3 -> 1.22.2	No	Proof of Concept
	399/1000 Why? Has a fix available, CVSS 3.7	Buffer Overflow SNYK-PYTHON-NUMPY-2321966	numpy: 1.21.3 -> 1.22.2	No	No Known Exploit
	506/1000 Why? Proof of Concept exploit, Has a fix available, CVSS 3.7	Denial of Service (DoS) SNYK-PYTHON-NUMPY-2321970	numpy: 1.21.3 -> 1.22.2	No	Proof of Concept

(*) Note that the real score may have changed since the PR was raised.

Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

Check the changes in this PR to ensure they won't cause issues with your project.

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PR summary

The Pull Request aims to upgrade the numpy package from version 1.21.3 to 1.22.2 in the gpt4all-training/requirements.txt file to address security vulnerabilities. Specifically, it fixes issues related to NULL Pointer Dereference, Buffer Overflow, and Denial of Service (DoS). The PR was automatically created by Snyk.

Suggestion

• Ensure that the upgrade to numpy 1.22.2 does not introduce any compatibility issues with other packages in the project.
• Consider addressing the warnings about missing dependencies (scipy, pillow) and the version conflict with dill (0.3.6 installed, but 0.3.7 or higher is required by multiprocess).
• Verify that the project's functionality remains intact after the upgrade by running tests and checking for any deprecated features or breaking changes in numpy 1.22.2.
• If the project relies on specific features of numpy 1.21.3 that are not compatible with 1.22.2, consider finding alternative solutions or refactoring the code to work with the newer version.

Disclaimer: This comment was entirely generated using AI. Be aware that the information provided may be incorrect.

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