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allegroai
2019-08-03 03:04:48 +03:00
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@@ -17,7 +17,7 @@ your experimentation logs, outputs, and data to one centralized server.
## Main Features
* Integrate with your current work flow with minimal effort
* Seamless integration with leading frameworks, including: *PyTorch*, *TensorFlow*, *Keras*, and others coming soon
* Seamless integration with leading frameworks, including: *PyTorch*, *TensorFlow*, *Keras*, *XGBoost*, *SciKit-Learn* and others coming soon
* Support for *Jupyter Notebook* (see [trains-jupyter-plugin](https://github.com/allegroai/trains-jupyter-plugin))
and *PyCharm* remote debugging (see [trains-pycharm-plugin](https://github.com/allegroai/trains-pycharm-plugin))
* Log everything. Experiments become truly repeatable
@@ -38,16 +38,17 @@ your experimentation logs, outputs, and data to one centralized server.
## TRAINS Automatically Logs
* Git repository, branch, commit id and entry point (git diff coming soon)
* Hyper-parameters, including
* Git repository, branch, commit id, entry point and local git diff
* Python packages (including specific version)
* Hyper-parameters
* ArgParser for command line parameters with currently used values
* Tensorflow Defines (absl-py)
* Explicit parameters dictionary
* Explicit parameters dictionary
* Tensorflow Defines (absl-py)
* Initial model weights file
* Model snapshots
* stdout and stderr
* StdOut and StdErr
* Tensorboard/TensorboardX scalars, metrics, histograms, images (with audio coming soon)
* Matplotlib
* Matplotlib & Seaborn
## How TRAINS Works