fix: PR comments

Author: AidanAbd

doc-requirements.txt

@@ -1,6 +1,6 @@
 #
-# This file is autogenerated by pip-compile with python 3.10
-# To update, run:
+# This file is autogenerated by pip-compile with Python 3.10
+# by the following command:
 #
 #    pip-compile doc-requirements.in
 #

docs/source/basics/defining_cloud_resources.md

@@ -9,15 +9,15 @@ RAM, Storage, GPU) at runtime and these requests will be fullfilled during the s
 process.
 
 ## CPU
-The number of cpu cores available to the task. If your task uses more than the alotted
+The number of cpu cores available to the task. If the task uses more than the alotted
 number of cores, the task can continue running but will be throttled.
 
 ## Memory
-The amount of memory available to the task. If your task uses more than the alotted
+The amount of memory available to the task. If the task uses more than the alotted
 amount of memory, the task will crash.
 
 ## Storage
-The amount of disc space available to the task. If your task uses more than the alotted
+The amount of disc space available to the task. If the task uses more than the alotted
 amount of storage, the task will crash.
 
 ## GPU
@@ -60,7 +60,7 @@ You can also specify task resources using `@custom_task`:
 ```python
 from latch import custom_task
 
-@custom_task(cpu, memory, storage_gib = storage_gib) # cpu: int, memory: int, storage_gib: int
+@custom_task(cpu=8, memory=64, storage_gib=1000)
 def my_task(
     ...
 ):

docs/source/getting_started/authoring_your_workflow.md

@@ -113,7 +113,7 @@ def my_task(
 ):
     ...
 
-@large_gpu_task #31 cpus, 120 gigs of memory, 2000 GiB of storage, 1 gpu
+@large_gpu_task # 31 cpus, 120 gigs of memory, 2000 GiB of storage, 1 gpu
 def inference(
     ...
 ):
@@ -126,7 +126,7 @@ To arbitrarily specify resource requirements, use:
 ```python
 from latch import custom_task
 
-@custom_task(cpu, memory, storage_gib = storage_gib)
+@custom_task(cpu=8, memory=64, storage_gib=1000)
 def my_task(
     ...
 ):

docs/source/index.md

@@ -24,7 +24,7 @@ Lacth SDK allows developers to upload workflows to the full-featured [Latch Plat
 
 With Latch SDK, developers can write the description to their workflow and customize input parameters using plain Markdown. Latch automatically parses the written text and Python function headers to compile a type-safe UI.
 
-**Specifying arbitrary cloud compute and storage resources for bioinformatics pipelines is difficult.** With the Latch SDK, there are several Python task decorators that easily allow you to define the resources available at runtime. The framework starts at 2 CPUs and 4 GiBs of memory and goes all the way to 95 CPUs, 490 GBs of memory, 4949 GiB of storage, and 1 GPU (24 GBs of VRAM, 9,216 CUDA cores) to easily handle all processing needs.
+**Specifying arbitrary cloud compute and storage resources for bioinformatics pipelines is difficult.** With the Latch SDK, there are several Python task decorators that easily allow you to define the resources available at runtime. The framework starts at 2 CPUs and 4 GiBs of memory and goes all the way to 95 CPUs, 490 GiBs of memory, 4949 GiB of storage, and 1 GPU (24 GiBs of VRAM, 9,216 CUDA cores) to easily handle all processing needs.
 
 **Bioinformatics tools face the challenges of irreproducibility.** The lack of proper versioning and dependencies management results in a long tail of poorly documented and unusable bioinformatics software tools.