SymetryML Documentation

Technical Requirements

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Technical Requirements

Technical Requirement for SymetryML

RequirementDescription
Docker EngineDocker Engine installed on the host
Operating SystemAny Linux distribution that supports Docker
GPU (optional)NVIDIA GPU with Compute Capability >= 3.5, NVIDIA drivers, and NVIDIA Container Toolkit
Spark (optional)External Spark 4.1.0 cluster

System Recommendations

RecommendationDescription
Spark Cluster worker memoryMinimum: 8 GB Recommended: 16 GB and more

SymetryML Memory Requirements

SymetryML memory requirements depend on the number of projects and the number of attributes in each project. The following table provides a rough guideline as to the compute and memory requirements for your projects. These numbers are for dense datasets. For sparse datasets, the number will vary.

Number of AttributesType of ProjectRAM Memory Requirement
10CPU512m
64CPU512m
128CPU or GPU1g
512GPU1g
1024GPU1g
4096GPU or multi-GPU4g
8192GPU or multi-GPU16g
12,500GPU or multi-GPU24g
20,000-25,000multi-GPU64g
25,000-100,000multi-GPU on Nvidia V10064G to 256G

Data Source Requirements

Data SourceDescription
JDBC / ODBC
S3
SFTP
HTTP / HTTPS
Local FileLocal file where the application server resides
Kafka Streams
RedShift
PluginsIt's possible to add new 'plugin' data source to SymetryML
SymetryML Rest APIOne can use the SymetryML Rest API to push data into a SymetryML project. Please consult the following section for details (section called Real Time Streaming of Data into SymetryML Projects).

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