Clearml

Features and Bug Fixes
* Fix Jenkins CI/CD support

* Experiment code execution detection
- Automatically create package requirements section (including used versions)
- Automatically detect and store source code uncommitted changes
- Jupyter notebook support, automatically convert notebook to python script (stored under uncommitted changes)
- Jupyter notebook support, automatically update used packages in notebook (including used versions)
* Add resource monitoring to experiment metrics
- Sampled every 500ms, averaged over 30 seconds
- CPU / Network / IO / Memory etc.
- For GPU support please install gpustat
(currently not part of the requirements due to gpustat compatibility issues with Windows)
$ pip install gpustat

* Automatically stop inactive experiments (default: 2 hours)
* Improved visibility
- Finer status definitions: Identify successful completion vs. user aborted
- Experiment plot comparison: Ensure different colors for different experiments
- Parse newline character in experiment description
- Show experiment start time in table display
- Add vertical guide in scalar plots
- Move Hyper-parameters to designated tab
- "Admin" section now named "Profile"

Features and Bug Fixes
* Improved Jupyter and inline Matplotlib support
* Allow for insecure SSL connection to trains-server (use with care!)
* Automatically announce new *trains* version available (at least until we hit version 1.0)
* Fix support for local git branches (i.e. no matching remote branch)
* Verified support for Official TensorFlow 1.14 release and PyTorch with TensorBoard

Features and Bug Fixes

* Better support for Windows

Features and Bug Fixes
* Better support for python 2.7

Redesign Release

**Notice: This release is not backwards compatible**


* Easy to deploy & configure
* Support Machine Learning Models (Scikit Learn, XGBoost, LightGBM)
* Support Deep Learning Models (Tensorflow, PyTorch, ONNX)
* Customizable RestAPI for serving (i.e. allow per model pre/post-processing for easy integration)
* Flexible
* On-line model deployment
* On-line endpoint model/version deployment (i.e. no need to take the service down)
* Per model standalone preprocessing and postprocessing python code
* Scalable
* Multi model per container
* Multi models per serving service
* Multi-service support (fully separated multiple serving service running independently)
* Multi cluster support
* Out-of-the-box node auto-scaling based on load/usage
* Efficient
* multi-container resource utilization
* Support for CPU & GPU nodes
* Auto-batching for DL models
* Automatic deployment
* Automatic model upgrades w/ canary support
* Programmable API for model deployment
* Canary A/B deployment
* Online Canary updates
* Model Monitoring
* Usage Metric reporting
* Metric Dashboard
* Model performance metric
* Model performance Dashboard


Features:
- [x] FastAPI integration for inference service
- [x] multi-process Gunicorn for inference service
- [x] Dynamic preprocess python code loading (no need for container/process restart)
- [x] Model files download/caching (http/s3/gs/azure)
- [x] Scikit-learn. XGBoost, LightGBM integration
- [x] Custom inference, including dynamic code loading
- [x] Manual model upload/registration to model repository (http/s3/gs/azure)
- [x] Canary load balancing
- [x] Auto model endpoint deployment based on model repository state
- [x] Machine/Node health metrics
- [x] Dynamic online configuration
- [x] CLI configuration tool
- [x] Nvidia Triton integration
- [x] GZip request compression
- [x] TorchServe engine integration
- [x] Prebuilt Docker containers (dockerhub)
- [x] Docker-compose deployment (CPU/GPU)
- [x] Scikit-Learn example
- [x] XGBoost example
- [x] LightGBM example
- [x] PyTorch example
- [x] TensorFlow/Keras example
- [x] Model ensemble example
- [x] Model pipeline example
- [x] Statistics Service
- [x] Kafka install instructions
- [x] Prometheus install instructions
- [x] Grafana install instructions