better docs

README.md

@@ -17,15 +17,20 @@
 A framework for easy application of established machine learning techniques on large, multi-dimensional images.
 
 `dacapo` allows you to configure machine learning jobs as combinations of
-[DataSplits](http://docs/api.html#datasplits),
-[Architectures](http://docs/api.html#architectures),
-[Tasks](http://docs/api.html#tasks),
-[Trainers](http://docs/api.html#trainers),
+[DataSplits](https://janelia-cellmap.github.io/dacapo/autoapi/dacapo/experiments/datasplits/index.html),
+[Architectures](https://janelia-cellmap.github.io/dacapo/autoapi/dacapo/experiments/architectures/index.html),
+[Tasks](https://janelia-cellmap.github.io/dacapo/autoapi/dacapo/experiments/tasks/index.html),
+[Trainers](https://janelia-cellmap.github.io/dacapo/autoapi/dacapo/experiments/trainers/index.html),
 on arbitrarily large volumes of
 multi-dimensional images. `dacapo` is not tied to a particular learning
 framework, but currently only supports [`torch`](https://pytorch.org/) with
 plans to support [`tensorflow`](https://www.tensorflow.org/).
 
+
+![DaCapo Diagram](https://raw.githubusercontent.com/janelia-cellmap/dacapo/main/docs/source/_static/dacapo_diagram.png)
+
+
+
 ## Installation and Setup
 Currently, python>=3.10 is supported. We recommend creating a new conda environment for dacapo with python 3.10.
 ```

docs/source/notebooks/minimal_tutorial.py

@@ -340,6 +340,8 @@
 # %%
 import zarr
 
+run_path = config_store.path / run_config.name
+
 num_snapshots = run_config.num_iterations // run_config.trainer_config.snapshot_interval
 fig, ax = plt.subplots(num_snapshots, 3, figsize=(10, 2 * num_snapshots))
 
@@ -367,8 +369,6 @@
 # Visualize validations
 import zarr
 
-run_path = config_store.path / run_config.name
-
 num_validations = run_config.num_iterations // run_config.validation_interval
 fig, ax = plt.subplots(num_validations, 4, figsize=(10, 2 * num_validations))