PyPGX

Parallel Graph AnalytiX 21.4 for CPU on Python 3.8 (version 2.0)

A description of the Parallel Graph AnalytiX 21.4 for CPU on Python 3.8 (version 2.0) conda environment.


Released	March 29, 2022
Description	Python Parallel Graph AnalytiX (PyPGX) is a graph toolkit that provides a graph query language, optimized analytic algorithms, and graph machine learning. You can use it to extract hidden insights in datasets at scale and with high performance. Graph analysis is a data analysis methodology in which the dataset is represented as a graph. The graph vertices correspond to the data entities and edges to the relationships between them. Analyzing these graphs takes into account the fine-grained, arbitrary relationships that naturally occur in many datasets. This also enables the discovery of valuable insights about the data. To get started with this conda environment, review the `getting-started.ipynb` notebook example, Using the Notebook Explorer to access Notebook Examples.
Python Version	3.8
Slug	`pypgx214_p38_cpu_v2`
Object Storage Path	`oci://service-conda-packs@id19sfcrra6z/service_pack/cpu/Parallel Graph AnalytiX 21.4 for CPU on Python 3.8/2.0/pypgx214_p38_cpu_v2`
Top Libraries	oracle-ads (v2.5.8) pypgx (v21.4.4) python-igraph (v0.9.9) category_encoders (v2.3.0) For a complete list of preinstalled Python libraries, see pypgx214_p38_cpu_v2.
Example Notebooks	getting-started.ipynb ads_feature_type.ipynb ads_feature_type_custom.ipynb ads_feature_type_EDA.ipynb ads_feature_type_manager.ipynb ads_feature_type_validator.ipynb ads_feature_type_warnings.ipynb api_keys.ipynb caltech.ipynb model_catalog.ipynb model_deployment.ipynb model_deployment_using_jobs.ipynb project.ipynb pypgx_algorithms.ipynb pypgx_graph_ml.ipynb pypgx_pgql.ipynb vault.ipynb visual_genome.ipynb Using the Notebook Explorer to access Notebook Examples describes how to locate and access the included interactive example notebooks, and what each of them can be used for.

Parallel Graph AnalytiX 21.4 for CPU on Python 3.8 (version 1.0) [Removed]

Important

This conda environment has been removed due to a critical vulnerability within the Apache Log4j module (CVE-2021-44228).

If you have created published conda environments by cloning this environment, we strongly encourage you to remediate the vulnerability.

A description of the Parallel Graph AnalytiX 21.4 for CPU on Python 3.8 (version 1.0) conda environment.


Released	December 15, 2021
Description	Python Parallel Graph AnalytiX (PyPGX) is a graph toolkit that provides a graph query language, optimized analytic algorithms, and graph machine learning. You can use it to extract hidden insights in datasets at scale and with high performance. Graph analysis is a data analysis methodology in which the dataset is represented as a graph. The graph vertices correspond to the data entities and edges to the relationships between them. Analyzing these graphs takes into account the fine-grained, arbitrary relationships that naturally occur in many datasets. This also enables the discovery of valuable insights about the data. To get started with this conda environment, review the `getting-started.ipynb` notebook example, Using the Notebook Explorer to access Notebook Examples.
Python Version	3.8
Slug	`pypgx214_p38_cpu_v1`
Object Storage Path	`oci://service-conda-packs@id19sfcrra6z/service_pack/cpu/Parallel Graph AnalytiX 21.4 for CPU on Python 3.8/1.0/pypgx214_p38_cpu_v1`
Top Libraries	oracle-ads (v2.5.4) pypgx (v21.4.2) python-igraph (v0.9.8) For a complete list of preinstalled Python libraries, see pypgx214_p38_cpu_v1.
Example Notebooks	getting-started.ipynb api_keys.ipynb model_catalog.ipynb model_deployment.ipynb project.ipynb pypgx_algorithms.ipynb pypgx_graph_ml.ipynb pypgx_pgql.ipynb vault.ipynb Using the Notebook Explorer to access Notebook Examples describes how to locate and access the included interactive example notebooks, and what each of them can be used for.

Parallel Graph AnalytiX 21.3 for CPU on Python 3.8 (version 1.0) [Removed]

Important

This conda environment has been removed due to a critical vulnerability within the Apache Log4j module (CVE-2021-44228).

If you have created published conda environments by cloning this environment, we strongly encourage you to remediate the vulnerability.

A description of the Parallel Graph AnalytiX 21.3 for CPU on Python 3.8 (version 1.0) conda environment.


Released	July 14, 2021
Description	Python Parallel Graph AnalytiX (PyPGX) is a graph toolkit that provides a graph query language, optimized analytic algorithms, and graph machine learning. You can use it to extract hidden insights in datasets at scale and with high performance. Graph analysis is a data analysis methodology in which the dataset is represented as a graph. The graph vertices correspond to the data entities and edges to the relationships between them. Analyzing these graphs takes into account the fine-grained, arbitrary relationships that naturally occur in many datasets. This also enables the discovery of valuable insights about the data. To get started with this conda environment, review the `getting-started.ipynb` notebook example, Using the Notebook Explorer to access Notebook Examples.
Python Version	3.8
Slug	`pypgx213_p38_cpu_v1`
Object Storage Path	`oci://service-conda-packs@id19sfcrra6z/service_pack/cpu/Parallel Graph AnalytiX 21.3 for CPU on Python 3.8/1.0/pypgx213_p38_cpu_v1`
Top Libraries	oracle-ads (v2.5.8) oracle-ads (v2.2.1) pypgx (v21.3.1) python-igraph (v0.9.6) pandas (v1.2.5) For a complete list of preinstalled Python libraries, see pypgx213_p38_cpu_v1.txt.
Example Notebooks	getting-started.ipynb api_keys.ipynb model_catalog.ipynb model_deployment.ipynb project.ipynb pypgx_algorithms.ipynb pypgx_graph_ml.ipynb pypgx_pgql.ipynb vault.ipynb Using the Notebook Explorer to access Notebook Examples describes how to locate and access the included interactive example notebooks, and what each of them can be used for.

Oracle Cloud Infrastructure Documentation

PyPGX