@@ -39,7 +39,24 @@ Stanford and originally created by [Bharath Ramsundar](http://rbharath.github.io
Installation from source is the only currently supported format. ```deepchem``` currently supports both Python 2.7 and Python 3.5, but is not supported on any OS'es except 64 bit linux. Please make sure you follow the directions below precisely. While you may already have system versions of some of these packages, there is no guarantee that `deepchem` will work with alternate versions than those specified below.
### Full Anaconda distribution
### Using a conda environment
Alternatively, you can install deepchem in a new conda environment using the conda commands in scripts/install_deepchem_conda.sh
```bash
bash scripts/install_deepchem_conda.sh deepchem
pip install tensorflow-gpu==0.12.1 # If you want GPU support
git clone https://github.com/deepchem/deepchem.git # Clone deepchem source code from GitHub
cd deepchem
python setup.py install# Manual install
nosetests -v deepchem --nologcapture# Run tests
```
This creates a new conda environment `deepchem` and installs in it the dependencies that
are needed. To access it, use the `source activate deepchem` command.
Check [this link](https://conda.io/docs/using/envs.html) for more information about
the benefits and usage of conda environments. **Warning**: Segmentation faults can [still happen](https://github.com/deepchem/deepchem/pull/379#issuecomment-277013514)
via this installation procedure.
### 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)
@@ -108,22 +125,25 @@ Installation from source is the only currently supported format. ```deepchem```
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 conda environment
Alternatively, you can install deepchem in a new conda environment using the conda commands in scripts/install_deepchem_conda.sh
### Using a Docker Image
For major releases we will create docker environments with everything pre-installed
``` bash
bash scripts/install_deepchem_conda.sh deepchem
pip install tensorflow-gpu==0.12.1 # If you want GPU support
git clone https://github.com/deepchem/deepchem.git # Clone deepchem source code from GitHub
cd deepchem
python setup.py install# Manual install
nosetests -v deepchem --nologcapture# Run tests
# This will the deepchem docker image into your images
docker pull deepchemio/deepchem
# This will create a container out of our latest image
docker run -i-t deepchemio/deepchem
# You are now in a docker container whose python has deepchem installed
# For example you can run our tox21 benchmark
cd deepchem/examples
python benchmark.py -d tox21
# Or you can start playing with it in the command line
pip install jupyter
ipython
import deepchem as dc
```
This creates a new conda environment `deepchem` and installs in it the dependencies that
are needed. To access it, use the `source activate deepchem` command.
Check [this link](https://conda.io/docs/using/envs.html) for more information about
the benefits and usage of conda environments. **Warning**: Segmentation faults can [still happen](https://github.com/deepchem/deepchem/pull/379#issuecomment-277013514)
via this installation procedure.
## FAQ
1. Question: I'm seeing some failures in my test suite having to do with MKL
