nums 0.2.8
A numerical computing library for Python that scales.
Requires Python: <3.10,>=3.6

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# What is NumS?

**NumS** is a Numerical cloud computing library that translates Python and NumPy to distributed systems code at runtime. 
NumS scales NumPy operations horizontally, and provides inter-operation (task) parallelism for those operations.
NumS remains faithful to the NumPy API, and provides tight integration with the Python programming language 
by supporting loop parallelism and branching.
NumS' system-level operations are written against the [Ray](https://github.com/ray-project/ray) API;
it supports S3 and basic distributed filesystem operations for storage
and uses [NumPy](https://github.com/numpy/numpy) as a backend for CPU-based array operations.

# Usage

Obtain the latest release of NumS using `pip install nums`.

NumS provides explicit implementations of the NumPy API,
providing a clear API with code hinting when used in conjunction with
IDEs (e.g. PyCharm) and interpreters (e.g. iPython, Jupyter Notebook) 
that provide such functionality.

## Basics

Below is a quick snippet that simply samples a few large arrays and 
performs basic array operations.

```python
import nums.numpy as nps

# Compute some products.
x = nps.random.rand(10**8)
# Note below the use of `get`, which blocks the executing process until
# an operation is completed, and constructs a numpy array
# from the blocks that comprise the output of the operation.
print((x.T @ x).get())
x = nps.random.rand(10**4, 10**4)
y = nps.random.rand(10**4)
print((x @ y).shape)
print((x.T @ x).shape)

# NumS also provides a speedup on basic array operations,
# such array search.
x = nps.random.permutation(10**8)
idx = nps.where(x == 10**8 // 2)

# Whenever possible, NumS automatically evaluates boolean operations
# to support Python branching.
if x[idx] == 10**8 // 2:
    print("The numbers are equal.")
else:
    raise Exception("This is impossible.")
```

## I/O

NumS provides an optimized I/O interface for fast persistence of block arrays.
See below for a basic example.

```python
import nums
import nums.numpy as nps

# Write an 800MB object in parallel, utilizing all available cores and
# write speeds available to the OS file system.
x1 = nps.random.rand(10**8)
# We invoke `get` to block until the object is written.
# The result of the write operation provides status of the write
# for each block as a numpy array.
print(nums.write("x.nps", x1).get())

# Read the object back into memory in parallel, utilizing all available cores.
x2 = nums.read("x.nps")
assert nps.allclose(x1, x2)
```

NumS automatically loads CSV files in parallel as distinct arrays, 
and intelligently constructs a partitioned array for block-parallel linear algebra operations.


```python
# Specifying has_header=True discards the first line of the CSV.
dataset = nums.read_csv("path/to/csv", has_header=True)
```

##  Logistic Regression
In this example, we'll run logistic regression on a 
bimodal Gaussian. We'll begin by importing the necessary modules.

```python
import nums.numpy as nps
from nums.models.glms import LogisticRegression
```

NumS initializes its system dependencies automatically as soon as an operation is performed. 
Thus, importing modules triggers no systems-related initializations.

#### Parallel RNG

NumS is based on NumPy's parallel random number generators.
You can sample billions of random numbers in parallel, which are automatically 
block-partitioned for parallel linear algebra operations.

Below, we sample an 800MB bimodal Gaussian, which is asynchronously generated and stored
by the implemented system's workers.

```python
size = 10**8
X_train = nps.concatenate([nps.random.randn(size // 2, 2), 
                           nps.random.randn(size // 2, 2) + 2.0], axis=0)
y_train = nps.concatenate([nps.zeros(shape=(size // 2,), dtype=nps.int), 
                           nps.ones(shape=(size // 2,), dtype=nps.int)], axis=0)
```

#### Training

NumS's logistic regression API follows the scikit-learn API, a
familiar API to the majority of the Python scientific computing community.

```python
model = LogisticRegression(solver="newton-cg", penalty="l2", C=10)
model.fit(X_train, y_train)
```

We train our logistic regression model using the Newton method.
NumS's optimizer automatically optimizes scheduling of 
operations using a mixture of block-cyclic heuristics, and a fast, 
tree-based optimizer to minimize memory and network load across distributed memory devices.
For tall-skinny design matrices, NumS will automatically perform data-parallel
distributed training, a near optimal solution to our optimizer's objective.


#### Evaluation

We evaluate our dataset by computing the accuracy on a sampled test set.

```python
X_test = nps.concatenate([nps.random.randn(10**3, 2), 
                          nps.random.randn(10**3, 2) + 2.0], axis=0)
y_test = nps.concatenate([nps.zeros(shape=(10**3,), dtype=nps.int), 
                          nps.ones(shape=(10**3,), dtype=nps.int)], axis=0)
print("train accuracy", (nps.sum(y_train == model.predict(X_train)) / X_train.shape[0]).get())
print("test accuracy", (nps.sum(y_test == model.predict(X_test)) / X_test.shape[0]).get())
```

We perform the `get` operation to transmit 
the computed accuracy from distributed memory to "driver" (the locally running process) memory.

You can run this example in your browser [here](https://mybinder.org/v2/gh/nums-project/nums-binder-env/master?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Fnums-project%252Fnums%26urlpath%3Dtree%252Fnums%252Fexamples%252Fnotebooks%252Flogistic_regression.ipynb%26branch%3Dmaster).

#### Training on HIGGS

Below is an example of loading the HIGGS dataset
(download [here](https://archive.ics.uci.edu/ml/machine-learning-databases/00280/)), 
partitioning it for training, and running logistic regression.

```python
import nums
import nums.numpy as nps
from nums.models.glms import LogisticRegression

higgs_dataset = nums.read_csv("HIGGS.csv")
y, X = higgs_dataset[:, 0].astype(int), higgs_dataset[:, 1:]
model = LogisticRegression(solver="newton-cg")
model.fit(X, y)
y_pred = model.predict(X)
print("accuracy", (nps.sum(y == y_pred) / X.shape[0]).get())
```

# Installation
NumS releases are tested on Linux-based systems running Python 3.7, 3.8, and 3.9.

NumS runs on Windows, but not all features are tested. We recommend using Anaconda on Windows. Download and install Anaconda for Windows [here](https://docs.anaconda.com/anaconda/install/windows/). Make sure to add Anaconda to your PATH environment variable during installation.

#### pip installation
To install NumS on Ray with CPU support, simply run the following command.
```sh
pip install nums
```

#### conda installation
We are working on providing support for conda installations, but in the meantime,
run the following with your conda environment activated. 

```sh
pip install nums
# Run below to have NumPy use MKL.
conda install -fy mkl
conda install -fy numpy scipy
```

#### S3 Configuration
To run NumS with S3, 
configure credentials for access by following instructions here: 
https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-files.html

#### Cluster Setup
NumS programs can run on a single machine, and can also seamlessly scale to large clusters. \
Read more about [launching clusters](https://github.com/nums-project/nums/tree/master/cluster-setup).