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allegroai 2019-08-03 03:24:00 +03:00
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@ -24,21 +24,21 @@ your experimentation logs, outputs, and data to one centralized server.
**With only two lines of code, this is what you are getting:** **With only two lines of code, this is what you are getting:**
* Git repository, branch, commit id, entry point and local git diff * Git repository, branch, commit id, entry point and local git diff
* Python environment (including specific packages & versions) * Python environment (including specific packages & versions)
* StdOut and StdErr * StdOut and StdErr
* Hyper-parameters * Hyper-parameters
* ArgParser for command line parameters with currently used values * ArgParser for command line parameters with currently used values
* Explicit parameters dictionary * Explicit parameters dictionary
* Tensorflow Defines (absl-py) * Tensorflow Defines (absl-py)
* Initial model weights file * Initial model weights file
* Model snapshots * Model snapshots
* Tensorboard/TensorboardX scalars, metrics, histograms, images (with audio coming soon) * Tensorboard/TensorboardX scalars, metrics, histograms, images (with audio coming soon)
* Matplotlib & Seaborn * Matplotlib & Seaborn
* Supported frameworks: Tensorflow, PyTorch, Keras, XGBoost and Scikit-Learn (MxNet is coming soon) * Supported frameworks: Tensorflow, PyTorch, Keras, XGBoost and Scikit-Learn (MxNet is coming soon)
* Seamless integration (including version control) with **Jupyter Notebook** * Seamless integration (including version control) with **Jupyter Notebook**
and [*PyCharm* remote debugging](https://github.com/allegroai/trains-pycharm-plugin)) and [*PyCharm* remote debugging](https://github.com/allegroai/trains-pycharm-plugin)
**Detailed overview of TRAINS offering and system design can be found [Here](https://github.com/allegroai/trains/blob/master/docs/brief.md).** **Detailed overview of TRAINS offering and system design can be found [here](https://github.com/allegroai/trains/blob/master/docs/brief.md).**
## Using TRAINS ## Using TRAINS
@ -46,7 +46,7 @@ your experimentation logs, outputs, and data to one centralized server.
TRAINS is a two part solution: TRAINS is a two part solution:
1. TRAINS [python package](https://pypi.org/project/trains/) (auto-magically connects your code, see [Using TRAINS](https://github.com/allegroai/trains#using-trains)) 1. TRAINS [python package](https://pypi.org/project/trains/) (auto-magically connects your code, see [Using TRAINS](https://github.com/allegroai/trains#using-trains))
**TRAINS requires only two lines of code for full integration.** **TRAINS requires only two lines of code for full integration.**
To connect your code with TRAINS: To connect your code with TRAINS:
@ -69,7 +69,7 @@ TRAINS is a two part solution:
https://demoapp.trainsai.io/projects/76e5e2d45e914f52880621fe64601e85/experiments/241f06ae0f5c4b27b8ce8b64890ce152/output/log https://demoapp.trainsai.io/projects/76e5e2d45e914f52880621fe64601e85/experiments/241f06ae0f5c4b27b8ce8b64890ce152/output/log
- Open the link and view your experiment parameters, model and tensorboard metrics - Open the link and view your experiment parameters, model and tensorboard metrics
**See examples [here](https://github.com/allegroai/trains/tree/master/examples)** **See examples [here](https://github.com/allegroai/trains/tree/master/examples)**
2. [TRAINS-server](https://github.com/allegroai/trains-server) for logging, querying, control and UI ([Web-App](https://github.com/allegroai/trains-web)) 2. [TRAINS-server](https://github.com/allegroai/trains-server) for logging, querying, control and UI ([Web-App](https://github.com/allegroai/trains-web))