Updated README.ipynb (and generated README.md)

README.ipynb

@@ -8,6 +8,7 @@
     "\n",
     "# MakeAgent\n",
     "\n",
+    "WIP\n",
     "\n",
     "### Setup\n",
     "\n",

README.md

@@ -1,125 +1,12 @@
 <!-- Warning, README.md is autogenerated from README.ipynb, do not edit it directly -->
 
-`pip install lr_schedules`
+# MakeAgent
 
-[![](https://github.com/sradc/lr_schedules/workflows/Python%20package/badge.svg)](https://github.com/sradc/lr_schedules/commits/)
+WIP
 
-# lr_schedules
+### Setup
 
-This project currently just contains `LinearScheduler`, for custom linear learning rate schedules.
-
-
-```python
-from lr_schedules import LinearScheduler
-import matplotlib.pyplot as plt
-import torch
-```
-
-## PyTorch example, triangle
-
-
-```python
-times = [0, 0.5, 1]
-values = [0, 1, 0]
-
-W = torch.tensor([1.0], requires_grad=True)
-optimizer = torch.optim.SGD([W], lr=0.1)
-linear_scheduler = LinearScheduler(times, values, total_training_steps=100)
-scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, linear_scheduler)
-
-lr_vals = []
-for step in range(100):
-    optimizer.zero_grad()
-    loss = torch.sum(W**2)
-    loss.backward()
-    optimizer.step()
-    scheduler.step()
-    lr_vals.append(optimizer.param_groups[0]["lr"])
-
-plt.figure(figsize=(5, 2))
-plt.plot(lr_vals)
-plt.xlabel("Training step")
-plt.ylabel("Learning rate")
-plt.show()
-```
-
-
-
-![README_files/README_3_0.png](https://raw.githubusercontent.com/sradc/lr_schedules/master/README_files/README_3_0.png)
-
-
-
-## Pytorch example, ramp up and down
-
-
-```python
-times = [0, 0.1, 0.9, 1]
-values = [0, 1, 0.9, 0]
-
-W = torch.tensor([1.0], requires_grad=True)
-optimizer = torch.optim.SGD([W], lr=0.1)
-linear_scheduler = LinearScheduler(times, values, total_training_steps=100)
-scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, linear_scheduler)
-
-lr_vals = []
-for step in range(100):
-    optimizer.zero_grad()
-    loss = torch.sum(W**2)
-    loss.backward()
-    optimizer.step()
-    scheduler.step()
-    lr_vals.append(optimizer.param_groups[0]["lr"])
-
-plt.figure(figsize=(5, 2))
-plt.plot(lr_vals)
-plt.xlabel("Training step")
-plt.ylabel("Learning rate")
-plt.show()
-```
-
-
-
-![README_files/README_5_0.png](https://raw.githubusercontent.com/sradc/lr_schedules/master/README_files/README_5_0.png)
-
-
-
-## Pytorch example, specifying absolute number of steps
-
-
-```python
-times = [0, 12, 90, 100]
-values = [0, 1, 0.8, 0]
-
-W = torch.tensor([1.0], requires_grad=True)
-optimizer = torch.optim.SGD([W], lr=0.1)
-linear_scheduler = LinearScheduler(times, values)
-scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, linear_scheduler)
-
-lr_vals = []
-for step in range(100):
-    optimizer.zero_grad()
-    loss = torch.sum(W**2)
-    loss.backward()
-    optimizer.step()
-    scheduler.step()
-    lr_vals.append(optimizer.param_groups[0]["lr"])
-
-plt.figure(figsize=(5, 2))
-plt.plot(lr_vals)
-plt.xlabel("Training step")
-plt.ylabel("Learning rate")
-plt.show()
-```
-
-
-
-![README_files/README_7_0.png](https://raw.githubusercontent.com/sradc/lr_schedules/master/README_files/README_7_0.png)
-
-
-
-## Dev set up of repo
-
-- Clone the repo
-- Install `poetry` (repo was run with python3.9)
-- Run `poetry install --with docs`
+- Clone the repo and `cd` into it
+- Run `poetry install`
 - Run `poetry run pre-commit install`
+