qgate-sln-mlrun 0.2.1

The quality gate for testing MLRun/Iguazio solution.

QGate-Sln-MLRun

The Quality Gate for solution MLRun (and Iguazio). The main aims of the project are:

• independent quality test (function, integration, acceptance, ... tests)
• deeper quality checks before full rollout/use in company environments
• identification of possible compatibility issues (if any)
• external and independent test coverage
• etc.

The tests use these key components, MLRun solution see GIT mlrun, sample meta-data model see GIT qgate-model and this project.

Test scenarios

The quality gate covers these test scenarios (✅ done, ✔ in-progress, ❌ planned):

• Project
  • ✅ TS101: Create project(s)
  • ✅ TS102: Delete project(s)
• Feature set
  • ✅ TS201: Create feature set(s)
  • ✅ TS202: Create feature set(s) & Ingest from DataFrame source (one step)
  • ✅ TS203: Create feature set(s) & Ingest from CSV source (one step)
  • ✅ TS204: Create feature set(s) & Ingest from Parquet source (one step)
  • ❌ TS205: Create feature set(s) & Ingest from SQL source (one step)
  • ❌ TS206: Create feature set(s) & Ingest from Kafka source (one step)
  • ❌ TS207: Create feature set(s) & Ingest from HTTP source (one step)
• Ingest data
  • ✅ TS301: Ingest data to feature set(s) from DataFrame source
  • ✅ TS302: Ingest data to feature set(s) from CSV source
  • ✅ TS303: Ingest data to feature set(s) from Parquet source
  • ✔ TS304: Ingest data to feature set(s) from SQL source
  • ❌ TS305: Ingest data to feature set(s) from Kafka source
  • ❌ TS306: Ingest data to feature set(s) from HTTP source
• Feature vector
  • ✅ TS401: Create feature vector(s)
• Get data from vector
  • ✅ TS501: Get data from off-line feature vector(s)
  • ✅ TS502: Get data from on-line feature vector(s)
• Pipelines
  • ❌ TS601: Simple pipeline for DataFrame source
  • ❌ TS602: Simple pipeline for CSV source
  • ❌ TS603: Simple pipeline for Parquet source
  • ❌ TS604: Complex pipeline for DataFrame source
  • ❌ TS605: Complex pipeline for CSV source
  • ❌ TS606: Complex pipeline for Parquet source
• Build model
  • ✅ TS701: Build CART model
  • ❌ TS702: Build XGBoost model
  • ❌ TS703: Build DNN model
• Serve model
  • ✅ TS801: Serving score from CART
  • ❌ TS802: Serving score from XGBoost
  • ❌ TS803: Serving score from DNN

NOTE: Each test scenario contains addition specific test cases (e.g. with different targets for feature sets, etc.).

Test inputs/outputs

The quality gate tests these inputs/outputs (✅ done, ✔ in-progress, ❌ planned):

• Outputs (targets)
  • ✅ RedisTarget, ✔ SQLTarget/MySQL, ✔ SQLTarget/Postgres, ✅ KafkaTarget
  • ✅ ParquetTarget, ✅ CSVTarget
  • ✅ File system, ❌ S3, ❌ BlobStorage
• Inputs (sources)
  • ✅ Pandas/DataFrame, ❌ SQLSource/MySQL, ❌ SQLSource/Postgres, ❌ KafkaSource
  • ✔ ParquetSource, ✅ CSVSource
  • ✅ File system, ❌ S3, ❌ BlobStorage

The supported sources/targets from MLRun.

Sample of outputs

The reports in original form, see:

• all DONE - HTML, TXT
• with ERR - HTML, TXT

Usage

You can easy use this solution in four steps:

1. Download content of these two GIT repositories to your local environment
   • qgate-sln-mlrun
   • qgate-model
2. Update file qgate-sln-mlrun.env from qgate-model
   • Update variables for MLRun/Iguazio, see MLRUN_DBPATH, V3IO_USERNAME, V3IO_ACCESS_KEY, V3IO_API
     • setting of V3IO_* is needed only in case of Iguazio installation (not for pure free MLRun)
   • Update variables for QGate, see QGATE_* (basic description directly in *.env)
     • detail setup configuration
3. Run from qgate-sln-mlrun
   • python main.py
4. See outputs (location is based on QGATE_OUTPUT in configuration)
   • './output/qgt-mlrun-<date_time>.html'
   • './output/qgt-mlrun-<date_time>.txt'

Precondition: You have available MLRun or Iguazio solution (MLRun is part of that), see official installation steps, or directly installation for Desktop Docker.

Tested with

The project was tested with these MLRun versions (see change log):

• MLRun (in Desktop Docker)
  • MLRun 1.7.0 (plan 05/2024)
  • MLRun 1.6.3 (plan 04/2024), 1.6.2, 1.6.1, 1.6.0
  • MLRun 1.5.2, 1.5.1, 1.5.0
  • MLRun 1.4.1, 1.3.0
• Iguazio (k8s, on-prem, VM on VMware)
  • Iguazio 3.5.3 (with MLRun 1.4.1)
  • Iguazio 3.5.1 (with MLRun 1.3.0)

NOTE: Current state, only the last MLRun/Iguazio versions are valid for testing (these tests are without back-compatibilities).

Others

• To-Do, the list of expected/future improvements, see
• Applied limits, the list of applied limits, see
• How can you test the solution?, you have to focus on Linux env. or Windows with WSL2 (see step by step tutorial)
• MLRun/Iguazio, the key changes in a nutshell, see