merge conflicts

cd deepchem

bash scripts/install_deepchem_conda.sh deepchem

source activate deepchem

conda install -c conda-forge tensorflow-gpu=1.3.0      # If you want GPU support

yes | pip install tensorflow-gpu==1.4.0      # If you want GPU support

python setup.py install                                # Manual install

nosetests -a '!slow' -v deepchem --nologcapture        # Run tests

```

### Easy Install via Conda

```bash

conda install -c deepchem -c rdkit -c conda-forge -c omnia deepchem=1.3.0

conda install -c deepchem -c rdkit -c conda-forge -c omnia deepchem=1.3.1

```

### Installing Dependencies Manually

1. Download the **64-bit** Python 2.7 or Python 3.5 versions of Anaconda for linux [here](https://www.continuum.io/downloads#_unix).

   Follow the [installation instructions](http://docs.continuum.io/anaconda/install#linux-install)

2. `rdkit`

   ```bash

   conda install -c rdkit rdkit

   ```

3. `joblib`

   ```bash

   conda install joblib

   ```

4. `six`

   ```bash

   pip install six

   ```

5. `networkx`

   ```bash

   conda install -c anaconda networkx=1.11

   ```

6. `mdtraj`

   ```bash

   conda install -c omnia mdtraj

   ```

7. `pdbfixer`

   ```bash

   conda install -c omnia pdbfixer=1.4

   ```

8. `tensorflow`: Installing `tensorflow` on older versions of Linux (which

    have glibc < 2.17) can be very challenging. For these older Linux versions,

    contact your local sysadmin to work out a custom installation. If your

    version of Linux is recent, then the following command will work:

    ```

    pip install tensorflow-gpu==1.3.0

    ```

9. `deepchem`: Clone the `deepchem` github repo:

   ```bash

   git clone https://github.com/deepchem/deepchem.git

   ```

   `cd` into the `deepchem` directory and execute

   ```bash

   python setup.py install

   ```

10. To run test suite, install `nosetests`:

   ```bash

   pip install nose

   ```

   Make sure that the correct version of `nosetests` is active by running

   ```bash

   which nosetests

   ```

   You might need to uninstall a system install of `nosetests` if

   there is a conflict.

11. If installation has been successful, all tests in test suite should pass:

    ```bash

    nosetests -v deepchem --nologcapture

    ```

    Note that the full test-suite uses up a fair amount of memory.

    Try running tests for one submodule at a time if memory proves an issue.

### Using a Docker Image

For major releases we will create docker environments with everything pre-installed.

In order to get GPU support you will have to use the 

[nvidia-docker](https://github.com/NVIDIA/nvidia-docker) plugin.

Using a docker image requires an NVIDIA GPU.  If you do not have a GPU please follow the directions for [using a conda environment](#using-a-conda-environment)

In order to get GPU support you will have to use the [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) plugin.

``` bash

# This will the download the latest stable deepchem docker image into your images

docker pull deepchemio/deepchem

   ```

## Getting Started

The first step to getting started is looking at the examples in the `examples/` directory. Try running some of these examples on your system and verify that the models train successfully. Afterwards, to apply `deepchem` to a new problem, try starting from one of the existing examples and modifying it step by step to work with your new use-case.

Two good tutorials to get started are [Graph Convolutional Networks](https://deepchem.io/docs/notebooks/graph_convolutional_networks_for_tox21.html) and [Multitask_Networks_on_MUV](https://deepchem.io/docs/notebooks/Multitask_Networks_on_MUV.html). Follow along with the tutorials to see how to predict properties on molecules using neural networks.

Afterwards you can go through other [tutorials](https://deepchem.io/docs/notebooks/index.html), and look through our examples in the `examples` directory. To apply `deepchem` to a new problem, try starting from one of the existing examples or tutorials and modifying it step by step to work with your new use-case. If you have questions or comments you can raise them on our [gitter](https://gitter.im/deepchem/Lobby).

### Input Formats

Accepted input formats for deepchem include csv, pkl.gz, and sdf files. For

            capacity=5,

            dtypes=[tf.float32] *

            (len(label_placeholders) + len(weight_placeholders) + 1))

        graph.enqueue = graph.queue.enqueue([mol_features] + label_placeholders

                                            + weight_placeholders)

        graph.enqueue = graph.queue.enqueue(

            [mol_features] + label_placeholders + weight_placeholders)

        queue_outputs = graph.queue.dequeue()

        labels = queue_outputs[1:len(label_placeholders) + 1]

        weights = queue_outputs[len(label_placeholders) + 1:]

            capacity=5,

            dtypes=[tf.float32] *

            (len(label_placeholders) + len(weight_placeholders) + 1))

        graph.enqueue = graph.queue.enqueue([mol_features] + label_placeholders

                                            + weight_placeholders)

        graph.enqueue = graph.queue.enqueue(

            [mol_features] + label_placeholders + weight_placeholders)

        queue_outputs = graph.queue.dequeue()

        labels = queue_outputs[1:len(label_placeholders) + 1]

        weights = queue_outputs[len(label_placeholders) + 1:]

            capacity=5,

            dtypes=[tf.float32] *

            (len(label_placeholders) + len(weight_placeholders) + 1))

        graph.enqueue = graph.queue.enqueue([mol_features] + label_placeholders

                                            + weight_placeholders)

        graph.enqueue = graph.queue.enqueue(

            [mol_features] + label_placeholders + weight_placeholders)

        queue_outputs = graph.queue.dequeue()

        labels = queue_outputs[1:len(label_placeholders) + 1]

        weights = queue_outputs[len(label_placeholders) + 1:]

            capacity=5,

            dtypes=[tf.float32] *

            (len(label_placeholders) + len(weight_placeholders) + 1))

        graph.enqueue = graph.queue.enqueue([mol_features] + label_placeholders

                                            + weight_placeholders)

        graph.enqueue = graph.queue.enqueue(

            [mol_features] + label_placeholders + weight_placeholders)

        queue_outputs = graph.queue.dequeue()

        labels = queue_outputs[1:len(label_placeholders) + 1]

        weights = queue_outputs[len(label_placeholders) + 1:]

This directory contains broken examples which no longer work. These ipython notebooks contain some useful pedagogical material despite having broken code.

Tips:

To use images in your code use

from IPython.display import Image, display

display(Image(filename='filename'))

Your notebook name cannot contain spaces

The first cell has to be markdown type and will

be the title of the tutorial