Small Changes for SCDL release (#1051)

1. Adding to README
2. pin to correct bionemo-core
3. update SCDL version

---------

Signed-off-by: Polina Binder <pbinder@nvidia.com>

Author: polinabinder1

sub-packages/bionemo-scdl/README.md

@@ -163,13 +163,9 @@ convert_h5ad_to_scdl --data-path hdf5s --save-path example_dataset
 
 ## Runtimes with SCDL
 
-The runtime and memory usage are examined on a CellXGene Dataset with ~1.5 million rows and a size of 24 GB. On this dataset, there is a 4.9x memory speed up.
+The runtime is examined on the Tahoe 100M dataset,  which containes over 100 million rows. On this dataset, there is either a 12x or 53x speed up depending on the machine used.
 
-![Throughput Image](https://raw.githubusercontent.com/NVIDIA/bionemo-framework/main/sub-packages/bionemo-scdl/assets/throughput.png)
-
-Additionally, the peak memory usage when iterating over the datasets with the SCDL dataloader is only 36.5 MB, since the whole dataset is never loaded into memory due to the numpy memomory-mapped backing.
-
-![Memory Image](https://raw.githubusercontent.com/NVIDIA/bionemo-framework/main/sub-packages/bionemo-scdl/assets/disk_space.png)
+![Throughput](https://raw.githubusercontent.com/NVIDIA/bionemo-framework/pbinder/scdl_add_to_edawson/sub-packages/bionemo-scdl/assets/tahoe_throughput.png)
 
 ### Using Neighbor Information in Single Cell Datasets
 

sub-packages/bionemo-scdl/VERSION

@@ -1 +1 @@
-0.0.7
+0.1.0

sub-packages/bionemo-scdl/assets/tahoe_throughput.png

@@ -23,7 +23,7 @@ dependencies = [
 
 [project.optional-dependencies]
 test = [
-    "bionemo-core>=2.4.0",
+    "bionemo-core>=2.4.5",
     'pytest>=8.4.1'
 ]