Trains

Features and Bug Fixes

* Add binding for `tensorboard.summarywriter.addscalars`
* Add `Logger.tensorboard_single_series_per_graph()` configuration option to allow per-series TensorBoard-like graphs
* Add docker image interface using `Task.set_base_docker()` and `Task.get_base_docker()`
* Use standard OS environment variables to obtain default credentials for:
- AWS: `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY` and `AWS_DEFAULT_REGION`
- Azure: `AZURE_STORAGE_ACCOUNT` and `AZURE_STORAGE_KEY`
- Google: `GOOGLE_APPLICATION_CREDENTIALS`
* Add simple dictionary task parameters interface using `Task.get_parameters_as_dict()` and `Task.set_parameters_as_dict()` allowing getting and setting parameters for task instances obtained using `Task.get_task()`
* Make sure `Task.connect()` always returns the connected instance passed to it
* Prefer `tensorflow_gpu` over `tensorflow` when inspecting installed packages and recording experiment dependencies
* Remove title/series naming restrictions (allow `$` and `.`) when reporting metrics
* Fix incorrect printouts in initialization wizard and upgrade notifications
* Fix debug images URL for uploaded files with `%` in their name

Breaking Changes
* Rename `trains.debugging.apply_args()` to `trains.debugging.apply_logging_args()`

Features and Bug Fixes
- Allow reporting a pre-uploaded image url in `Logger.report_image()` using the `url` parameter
- Add support for git repositories without ".git" suffix (e.g. [Azure Repos](https://azure.microsoft.com/en-us/services/devops/repos))
- Improve conda support
- Improve hyper-parameters argparser integration
- Fix `savefig()` patching in `matplotlib` binding
- Fix logs, events and jupyter flushing on exit

Features and Bug Fixes
- Add support for `pyplot.savefig` and `pylab.savefig` in matplotlib binding
- Add support for SageMaker
- Improve configuration wizard
- Try to make sure tensorboard is available when using torch
- Do not store keras model network design if it cannot be serialized 72
- Fix matplotlib binding support

Features and Bug Fixes
- Add support for [trains-server](https://github.com/allegroai/trains-server) v0.13.0
- Add support for nested (non-main) tasks
- Add warning when automatic argument parser binding cannot be turned off
- Add `Task.upload_artifact()` support for external URLs (pre-uploaded)
- Add support for special characters in hyper-parameter keys (white-spaces, `.` and `$`) 69
- Add support for PyTorch `.pt` model files
- Calculate data-audit artifact uniqueness by user-criteria 45
- Use an environment variable for setting a default docker image 58
- Improve `trains-init` configuration wizard
- Update examples for new joblib versions
- Update jupyter example to TensorFlow 2
- Fix task clone to copy only input artifacts
- Fix matplotlib import binding when using `Agg` backend
- Fix `ProxyDictPreWrite` and `ProxyDictPostWrite` so they can be pickled correctly 72
- Fix requests issue in Python 2.7 that can cause a deadlock when importing netrc
- Fix argparser binding sub-parser and type casting support 74
- Fix argparser binding python 2.7 unicode handling
- Fix unsynced connected hyper parameters overridden during remote execution

Features and Bug Fixes
- Add `default_output_uri` feature 57
- Improve `trains-init` configuration wizard
- Improve argparser binding
- Fix artifact support in data pipeline 63
- Fix threading issue while querying multiple experiments 64
- Fix uploading large files over slow HTTP connections
- Fix support for Git versions < 2

Breaking Changes
- Do not reuse an experiment with artifacts

Features and Bug Fixes
* Add `Task.connect_configuration()`, connect your configuration file (or configuration dictionary including nesting support). See [example](https://github.com/allegroai/trains/blob/master/examples/manual_model_config.py).
* Add `Task.connect_label_enumeration()`, store model detection classes together with the model & experiment
* Edit & override configuration file (or dictionary) content when executing with [trains-agent](https://github.com/allegroai/trains-agent)
* `Task.connect()` automatically supports nested dictionaries (e.g. `param/nested/key`) , including overriding values when running with [trains-agent](https://github.com/allegroai/trains-agent). See [example](https://github.com/allegroai/trains/blob/master/examples/manual_model_config.py).
* Add `Artifact.get()`, download & load an artifact.
* Add `Task.get_tasks()`, retrieve previous experiments, get metrics, rename etc.
* Improve Windows support
* Improve Minio support
* Fix Python2 support
* Fix Issue 56