DeepChem has a number of "soft" requirements. These are packages which are needed for various submodules of DeepChem but not for the package as a whole.
You can install deepchem in a new conda environment using the conda commands in `scripts/install_deepchem_conda.sh.` Installing via this script will ensure that you are **installing from the source**.
The following script requires `conda>=4.4` because it uses the `conda activate` command. (Please see the detail from [here](https://github.com/conda/conda/blob/a4c4feae404b2b378e106bd25f62cc8be15c768f/CHANGELOG.md#440-2017-12-20))
@@ -86,14 +89,8 @@ Then, follow each instruction on your OS.
Before activating deepchem environment, make sure conda-powershell has been initialized.
Check if there is a `(base)` before `PS` in powershell.
If not, use `conda init powershell` to activate it, then:
```
```bash
conda activate deepchem
python setup.py install
nosetests -a'!slow'-v deepchem --nologcapture
@@ -166,27 +155,33 @@ import deepchem as dc
1. DeepChem currently supports Python 3.5 through 3.7, and is supported on 64 bit Linux and Mac OSX. Note that DeepChem is not currently maintained for older versions of Python or with other operating systems.
2. Question: I'm seeing some failures in my test suite having to do with MKL
```Intel MKL FATAL ERROR: Cannot load libmkl_avx.so or libmkl_def.so.```
`Intel MKL FATAL ERROR: Cannot load libmkl_avx.so or libmkl_def.so.`
Answer: This is a general issue with the newest version of `scikit-learn` enabling MKL by default. This doesn't play well with many linux systems. See [BVLC/caffe#3884](https://github.com/BVLC/caffe/issues/3884) for discussions. The following seems to fix the issue
3. Note that when using Ubuntu 16.04 server or similar environments, you may need to ensure libxrender is provided via e.g.:
```bash
sudo apt-get install-y libxrender-dev
```
## Getting Started
The DeepChem project maintains an extensive colelction of [tutorials](https://github.com/deepchem/deepchem/tree/master/examples/tutorials). All tutorials are designed to be run on Google colab (or locally if you prefer). Tutorials are arranged in a suggested learning sequence which will take you from beginner to proficient at molecular machine learning and computational biology more broadly.
After working through the tutorials, you can also go through other [examples](https://github.com/deepchem/deepchem/tree/master/examples). 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).
### Gitter
Join us on gitter at [https://gitter.im/deepchem/Lobby](https://gitter.im/deepchem/Lobby). Probably the easiest place to ask simple questions or float requests for new features.
## About Us
DeepChem is managed by a team of open source contributors. Anyone is free to join and contribute!
## Citing DeepChem
@@ -206,4 +201,5 @@ To cite this book, please use this bibtex entry:
