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Add Git LFS to instructions #157

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May 5, 2022
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Add Git LFS to instructions #157

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16 changes: 9 additions & 7 deletions Task_1/README.md
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Expand Up @@ -8,6 +8,7 @@ Please ask any additional questions in our discussion pages on our github site a
### System requirements

1. [Git](https://git-scm.com/downloads).
2. [Git LFS](https://github.com/git-lfs/git-lfs#downloading)
2. Python with virtual environment management system: we recommend using [Anaconda](https://www.anaconda.com/products/individual).
3. **Windows- Only**: Pickle5 requires Microsoft C++ 14.0 or greater from the [C++ build tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/).

Expand All @@ -16,18 +17,19 @@ Please ask any additional questions in our discussion pages on our github site a
1. Register for the FeTS 2022 Challenge [here](https://www.synapse.org/#!Synapse:syn28546456/wiki/617093) and submit a data request.
2. ```git clone https://github.com/FETS-AI/Challenge.git```
3. ```cd Challenge/Task_1```
4. Create virtual environment (python 3.6-3.8): using Anaconda, a new environment can be created and activated using the following commands:
4. ```git lfs pull```
5. Create virtual environment (python 3.6-3.8): using Anaconda, a new environment can be created and activated using the following commands:
```bash
## create venv in specific path
conda create -p ./venv python=3.7 -y
conda activate ./venv
```
5. ```pip install --upgrade pip```
6. Install Pytorch LTS (1.8.2) for your system using [these instructions](https://pytorch.org/get-started/locally/)
7. ```pip install .```
8. ```python FeTS_Challenge.py```
9. All lower-level details are in the [FeTS Challenge python file](./FeTS_Challenge.ipy)
10. To view intermediate results with TensorBoard during training, you can run the following command:
6. ```pip install --upgrade pip```
7. Install Pytorch LTS (1.8.2) for your system using [these instructions](https://pytorch.org/get-started/locally/)
8. ```pip install .```
9. ```python FeTS_Challenge.py```
10. All lower-level details are in the [FeTS Challenge python file](./FeTS_Challenge.ipy)
11. To view intermediate results with TensorBoard during training, you can run the following command:
```tensorboard --logdir ~/.local/workspace/logs/tensorboard```

## Time to Convergence Metric (formerly "communication cost")
Expand Down