added docs page

docs/index.md

@@ -8,13 +8,36 @@
 - CPU example   
 - Single GPU example   
 - Multi-gpu example 
-- SLURM cluster example      
+- SLURM cluster grid search example      
 
+###### Training loop
+- Accumulate gradients
+- Check GPU usage
+- Check which gradients are nan
+- Check validation every n epochs
+- Display metrics in progress bar
+- Force training for min or max epochs
+- Inspect gradient norms
+- Hooks
+- Learning rate annealing
+- Make model overfit on subset of data
+- Multiple optimizers (like GANs)
+- Set how much of the training set to check (1-100%)
+- training_step function
+
+###### Validation loop
+- Display metrics in progress bar
+- hooks
+- Set how much of the validation set to check (1-100%)
+- Set validation check frequency within 1 training epoch (1-100%)
+- validation_step function
+- Why does validation run first for 5 steps?
 
 ###### Distributed training
 - Single-gpu      
 - Multi-gpu      
 - Multi-node   
+- 16-bit mixed precision
 
 ###### Checkpointing
 - Model saving
@@ -22,20 +45,6 @@
 
 ###### Computing cluster (SLURM)
 - Automatic checkpointing   
-- Automatic saving, loading   
+- Automatic saving, loading  
+- Running grid search on a cluster 
 - Walltime auto-resubmit   
-
-###### Common training use cases 
-- 16-bit mixed precision
-- Accumulate gradients
-- Check val many times during 1 training epoch
-- Check GPU usage
-- Check validation every n epochs
-- Check which gradients are nan
-- Inspect gradient norms
-- Learning rate annealing
-- Make model overfit on subset of data
-- Min, max epochs
-- Multiple optimizers (like GANs)
-- Run a sanity check of model val and tng step
-- Set how much of the tng, val, test sets to check (1-100%)