added dependency and pytest marker guidelines

to maintain seperate Python package environments, each of which can be tied

to a specific build of DeepChem. Here are some tips to get started:

1. Set up a new conda environment for DeepChem

```bash

conda create -n deepchem python=3.8

conda activate deepchem

```

2. Fork the [DeepChem](https://github.com/deepchem/deepchem/) repository

1. Fork the [DeepChem](https://github.com/deepchem/deepchem/) repository

and clone the forked repository

```bash

cd deepchem

```

 2.1. If you already have DeepChem from source, update it:

     1.1. If you already have DeepChem from source, update it by running

```bash

git fetch upstream

git rebase upstream/master

```

2. Set up a new conda environment for DeepChem

```bash

conda create -n deepchem python=3.8

conda activate deepchem

```

     2.1. DeepChem provides backend support with tensorflow, pytorch or jax.

For developemnt of DeepChem with one of the above backend, update the environment using

```bash

conda env update -n deepchem --file requirements/backend-name/env_backend-name.yml --prune

```

You need to replace `backend-name` with one of `tensorflow`, `pytorch` or `jax`. PyTorch

by default is installed with GPU support. For pytorch CPU, use the following command:

```bash

conda env update -n deepchem --file requirements/torch/env_torch.cpu.yml --prune

```

3. Install DeepChem in `develop` mode

```bash

as it is not installed`; run `python setup.py clean` and install in `develop` mode again.

Some other tips:

- DeepChem has a number of requirements which can be found [here](https://deepchem.readthedocs.io/en/latest/get_started/requirements.html).

- Every contribution must pass the unit tests. Some tests are

[marked](https://docs.pytest.org/en/6.2.x/example/markers.html) with custom

markers like `@pytest.mark.tensorflow`. This helps mainly in two ways: 1) restricting the tests only

to the part of code marked with the marker 2) giving

[flexibility](https://docs.pytest.org/en/6.2.x/example/markers.html) in running

the unit tests depending on the environment.

- DeepChem has a number of soft requirements which can be found [here](https://deepchem.readthedocs.io/en/latest/get_started/requirements.html).

- If a commit is simple and doesn't affect any code (keep in mind that some

docstrings contain code that is used in tests), you can add `[skip ci]`

(case sensitive) somewhere in your commit message to [skip all build /

### Pull Request Process

Every contribution, must be a pull request and must have adequate time for

review by other committers.

- [Flake8](https://flake8.pycqa.org/en/latest/) (code style check)

- [mypy](http://mypy-lang.org/) (type check)

- [doctest](https://docs.python.org/3/library/doctest.html) (interactive examples)

- [pytest](https://docs.pytest.org/en/6.2.x/index.html) (unit testing)

Before making a PR, please check your codes using them.

You can confirm how to check your codes from [Coding Conventions](https://deepchem.readthedocs.io/en/latest/development_guide/coding.html).

DeepChem uses [Sphinx](https://www.sphinx-doc.org/en/master/) to build

[the document](https://deepchem.readthedocs.io/en/latest/index.html).

The document is automatically built by [Numpy style docstrings]

(https://numpydoc.readthedocs.io/en/latest/format.html#numpydoc-docstring-guide)

The document is automatically built by

[Numpy style docstrings](https://numpydoc.readthedocs.io/en/latest/format.html#numpydoc-docstring-guide)

in source codes and [Napoleon extension](http://www.sphinx-doc.org/en/stable/ext/napoleon.html).

For any changes or modification to source code in a PR, please don't forget to add or modify Numpy style docstrings.