Fix spelling

Author: richelbilderbeek

.wordlist.txt

@@ -1119,3 +1119,5 @@ Ctrl
 rescaled
 UIs
 annot
+natively
+varanoi

docs/day4/gpu.rst

@@ -18,7 +18,7 @@ Using GPUs with Python
 Introduction
 ------------ 
 
-In order to understand the capabilities of a GPU, it is instructive to compare a pure CPU architecture with a GPU based architecture. Here, there is a schemematics of the former:
+In order to understand the capabilities of a GPU, it is instructive to compare a pure CPU architecture with a GPU based architecture. Here, there is a schematics of the former:
 
 .. figure:: ../img/AMD-Zen4-CPU-b-cn1701.png
    :align: center
@@ -51,7 +51,7 @@ One possible layout (Kebnekaise, AMD Zen4 node with L40s GPU) is as follows:
 
    Schematics of a hybrid CPU-GPU architecture. A GPU L40s card is attached to a NUMA island which in turn contains 24 cores (AMD Zen4 CPU node with 48 cores total). The NUMA island and the GPUs are connected through a PCI-E interconnect which makes the data transfer between both components rather slow.
 
-We can characterize the CPU and GPU performance with two quantities: the **latency** and the **througput**.
+We can characterize the CPU and GPU performance with two quantities: the **latency** and the **throughput**.
 
 - **Latency** refers to the time spent in a sole computation. 
 - **Throughput** denotes the number of computations that can be performed in parallel. Then, we can say that a CPU has low latency (able to do fast computations) but low throughput (only a few computations simultaneously).

docs/day4/ml.rst

@@ -397,7 +397,7 @@ Scikit-learn provides a comprehensive suite of tools for building and evaluating
 
    Try running ``titanic_sklearn.ipynb`` that can be found in ``Exercises/day4/MLDL`` directory, on an interactive CPU node. Also note that datasets are kept in ``Exercises/day4/MLDL/datasets`` directory. Give the **full path** to these datasets for this and subsequent Exercises.
 
-   Run it on a jupyter notebook on an interactive CPU node. An interative GPU node will also do. 
+   Run it on a jupyter notebook on an interactive CPU node. An interactive GPU node will also do. 
 
    Load the correct modules that contain scikit-learn, numpy, seaborn, pandas, matplotlib and jupyter libraries before starting the jupyter notebook. Users on NSC and PDC can build their own venvs.
    Use ``%matplotlib inline`` in jupyter to see the plots inline.
@@ -1398,6 +1398,6 @@ Exercises
 
   - At all clusters you will find PyTorch, TensorFlow, Scikit-learn under different modules, except Tetralith (NSC). 
   - When in doubt, search your modules and its correct version using ``module spider``.  If you still wished to have the correct versions for each cluster, check the `summary page <https://uppmax.github.io/HPC-python/summary2.html#summary-day2>`_.
-  - If you plan to use mutiple libraries with complex dependencies, it is recommended to use a virtual environment and pip install your libraries.
+  - If you plan to use multiple libraries with complex dependencies, it is recommended to use a virtual environment and pip install your libraries.
   - Always run heavy ML/DL jobs on compute nodes and not on login nodes. For development purpose, you can use an interactive session on a compute node.
 

docs/extra/isolated_deeper.rst

@@ -67,7 +67,7 @@ User
 ....
 
 - Create an environment based on dependencies given in an environment file
-- This can be done in new virtual environment or as a genera installtion locally (not activating any environment
+- This can be done in new virtual environment or as a genera installation locally (not activating any environment
 
 .. code-block:: console