@@ -30,7 +30,7 @@ materials science, quantum chemistry, and biology.
## Requirements
DeepChem requires these packages on any condition.
DeepChem currently supports Python 3.5 through 3.7 and requires these packages on any condition.
-[joblib](https://pypi.python.org/pypi/joblib)
-[NumPy](https://numpy.org/)
@@ -43,109 +43,49 @@ DeepChem requires these packages on any condition.
### Soft Requirements
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.
If you face some errors like `ImportError: No module named XXXX`, you may need to install some packages.
## Installation
### Install via conda (Recommendation)
Please check [the document](https://deepchem.readthedocs.io/en/latest/requirements.html##soft-requirements) about soft requirements.
RDKit is a soft requirement package, but many useful methods like molnet depend on it.
If you use conda, we recommend installing RDKit with deepchem.
## Installation
`deepchem>=2.4.0`
### Stable version
Coming soon...
**Caution!! : The latest stable version was published nearly a year ago. If you are a pip user or you face some errors, we recommend the nightly build version.**
`deepchem<2.4.0`
RDKit is a soft requirement package, but many useful methods like molnet depend on it. We recommend installing RDKit with deepchem.
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))
First, please clone the deepchem repository from GitHub.
RDKit is a soft requirement package, but many useful methods like molnet depend on it. We recommend installing RDKit with deepchem if you use conda.
Before activating deepchem environment, make sure conda has been initialized.
Check if there is a `(base)` in your command line. If not, use `conda init <YOUR_SHELL_NAME>` to activate it, then:
```bash
conda activate deepchem
python setup.py install
pytest -m"not slow" deepchem # optional
```
Check [this link](https://conda.io/projects/conda/en/latest/user-guide/install/index.html) for more information about the installation of conda environments.
### Install using a Docker
### Docker
If you want to install using a docker, you can pull two kinds of images.
If you want to install deepchem using a docker, you can pull two kinds of images.
- The latest image is built every time we commit to the master branch
- Dockerfile is put in `docker/master` directory
First, you pull the image you want to use.
You pull the image like this.
```bash
docker pull deepchemio/deepchem:2.3.0
```
Then, you create a container based on the image.
If you want to know docker usages with deepchem in more detail, please check [the document](https://deepchem.readthedocs.io/en/latest/installation.html#docker).
```bash
docker run --rm-it deepchemio/deepchem:2.3.0
```
If you want GPU support:
```bash
# If nvidia-docker is installed
nvidia-docker run --rm-it deepchemio/deepchem:2.3.0
docker run --runtime nvidia --rm-it deepchemio/deepchem:2.3.0
# If nvidia-container-toolkit is installed
docker run --gpus all --rm-it deepchemio/deepchem:2.3.0
```
You are now in a docker container which deepchem was installed. You can start playing with it in the command line.
```
(deepchem) root@xxxxxxxxxxxxx:~/mydir# python
Python 3.6.10 |Anaconda, Inc.| (default, May 8 2020, 02:54:21)
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import deepchem as dc
```
### From source
If you want to check the tox21 benchmark:
If you try install all soft dependencies at once or contribute to deepchem, we recommend you should install deepchem from source.
(deepchem) root@xxxxxxxxxxxxx:~/mydir# python benchmark.py -d tox21 -m graphconv -s random
```
## FAQ and Troubleshooting
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.`
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
