Because DNADNA has some non-trivial dependencies (most notably
PyTorch) the easiest way to install it is in a conda
We will show how to install it with
mamba (which is a faster
conda). Note that all following commands will work with
conda instead of
mamba, unless otherwise specified.
If you don’t have
conda yet, follow the official
to install either the full
distribution or, advisably, the smaller
Once you’ve installed
conda, you can follow the
You can install DNADNA in an existing conda environment, but the easiest way
is to install it with
mamba in a new environment. Start by creating a new
$ mamba create -n dnadna
where the name of the environment (
dnadna (it won’t install
Activate the environment with
mamba activate dnadna.
Then DNADNA can be installed and updated by running:
$ mamba install -c mlgenetics -c pytorch -c bioconda -c conda-forge dnadna
-c flag specifies a conda “channel” that needs to be searched for
some of the dependencies. If you want to add the “mlgenetics” channel and
those of its dependencies to your list of default conda channels, you can
$ conda config --add channels mlgenetics \ --add channels pytorch \ --add channels conda-forge \ --add channels bioconda
Note: The previous command works only with
conda, not with
One can make sure
dnadna is properly installed by running
dnadna -h and
dnadna simulation init.
To ensure TensorBoard support is enabled, you can install it in your active conda environment with:
$ mamba install -c conda-forge tensorboard
DNADNA can be used inside Jupyter notebooks. If you are using conda/mamba environments, you need to install jupyterlab in the same environement as DNADNA:
$ mamba activate dnadna $ mamba install -c conda-forge jupyterlab
If you prefer not to use conda, and to get DNADNA and its dependencies directly using PyPI+pip you can run:
$ pip install dnadna
Note: We still advise to install it within a conda environment.
Alternatively, you may clone the DNADNA git repository and install from there:
$ git clone https://gitlab.com/mlgenetics/dnadna.git $ cd dnadna $ mamba env create $ mamba activate dnadna $ pip install .
Or using the lighter CPU-only environment:
$ git clone https://gitlab.com/mlgenetics/dnadna.git $ cd dnadna $ mamba env create --file environment-cpu.yml $ mamba activate dnadna-cpu $ pip install .
If you plan to do development on the package, it is advisable to choose
git clone solution and install in “editable” mode by running instead:
$ pip install -e .
If installed with conda/mamba, DNADNA can be updated by running:
$ mamba update -c mlgenetics -c pytorch -c conda-forge dnadna
Or, if you already added the release channels to your default channels, with simply:
$ mamba update dnadna
Note for internal users/developers
A Dockerfile is also included for building a Docker image containing DNADNA (it is based on conda, so it essentially recreates the installation environment explained above, including GPU support. To build the image run:
$ docker build --tag dnadna .
Make sure to run this from the root of the repository, as the entire repository needs to be passed to the Docker build context.
If run without specifying any further commands, it will open a shell with
dnadna conda environment enabled:
$ docker run -ti dnadna
However, you can also run it non-interactively, e.g. by specifying the
dnadna command. Here it is also likely a good idea to mount the data
directory for your simulation/training files. For example:
$ docker run -t -v /path/to/my/data:/data --workdir /data dnadna dnadna init
Additional notes on the Docker image:
By default it logs in as a non-root user named “dnadna” with UID 1001 and GID also of 1001. If you start the container with the
--userflag and your own UID it will change the UID and GID of the “dnadna” user. Make sure to specify both a UID and a GID, otherwise the group of all files owned by “dnadna” will be changed to “root”. E.g., run:
docker run -u $(id -u):$(id -g).
The default workdir is
/home/dnadna/dnadnawhich contains the
dnadnapackage source code.
If you want to install your own conda packages (e.g. to use this container for development), after starting the container it is best to create a new conda environment cloned from the base environment, then re-install dnadna:
$ conda create -n dnadna --clone base $ conda activate dnadna $ pip install -e .
This is not done by default by the image because it would require additional start-up time in the non-development case. However, as a short-cut for the above steps you can run the container with
docker run -e DEV=1.
Afterwards, you can create a snapshot of this container in another terminal, e.g. by running
docker commit <my-container> dnadna-dev.
We will list here some errors that could appear at installation or running time.
Error: mamba or conda install fails, for example with
$ conda config --show-sources ==> .condarc <== channel_priority: strict channels: - conda-forge - bioconda ...
displays some registered channels and
strict, you can relax this constraint with:
$ conda config --set channel_priority false
and re-run the installation command. If it persists you could try
pip install dnadnainstead.
$ dnadna simulation init ... ImportError: libgsl.so.25: cannot open shared object file: No such file or directory
$ mamba install gsl=2.7.0 -c conda-forge
and re-run the dnadna command.
Error: userwarning about
Solution: deactivate and reactivate your environment
python >= 3.6
(For a complete list, see
After successful installation you should have a command-line utility called
$ dnadna --help usage: dnadna [-h] [COMMAND] dnadna version ... top-level command. See dnadna <sub-command> --help for help on individual sub-commands. optional arguments: -h, --help show this help message and exit --plugin PLUGIN load a plugin module; the plugin may be specified either as the file path to a Python module, or the name of a module importable on the current Python module path (i.e. sys.path); plugins are just Python modules which may load arbitrary code (new simulators, loss functions, etc.) during DNADNA startup); --plugin may be passed multiple times to load multiple plugins --trace-plugins enable tracing of plugin loading; for most commands this is enabled by default, but for other commands is disabled to reduce noise; this forces it to be enabled -V, --version show the dnadna version and exit sub-commands: init Initialize a new model training configuration and directory structure. preprocess Prepare a training run from an existing model training configuration. train Train a model on a simulation using a specified pre-processed training config. predict Make parameter predictions on existing SNP data using an already trained model. simulation Run a registered simulation.
This implements a number of different sub-commands for different training
and simulation steps. The
dnadna command can be used either starting with
an existing simulation dataset (which may need to be first be converted to
the DNADNA Dataset
or you may use
dnadna‘s simulator interface to create a new simulation
Here we step through the complete process from configuring and generating a simulation, to running data pre-processing on the simulation, and training a network based off that simulation.
Simulation initialization and configuration
To initialize a simulation, we must first generate a config file and output
folder for it, using the
dnadna simulation init command:
$ dnadna simulation init my_dataset one_event Writing sample simulation config to my_dataset/my_dataset_simulation_config.yml ... Edit the config file as needed, then run this simulation with the command: dnadna simulation run my_dataset/my_dataset_simulation_config.yml
This will create a directory in the current directory, named
and initialize it with a config file pre-populated with sample parameters
for the built-in one_event example
Before running the simulation we may want to adjust some of the parameters.
my_dataset/my_dataset_simulation_config.yml in your favorite text
editor. By default we see that
of 3 per scenario (in the one_event example
n_replicates is the number of independently simulated genomic regions). You can change it to
20 scenarios with
2 replicates, i.e. 100 simulations total, which is good for this quick demo but too low for training a real model. For a real training with enough computing resources, we would recommend changing these numbers to higher values such as
100 (2 million simulations which take a very long time). You may
also set the
seed option to seed the random number generator for
reproducible results. The resulting file (with none of the other settings
changed) should look like:
# my_dataset/my_dataset_simulation_config.yml data_root: . n_scenarios: 20 n_replicates: 2 seed: 2 ...
Running the simulation
Now to run the simulation we configured, we run
dnadna simulation run,
passing it the path to the config file we just edited. If you run this in a
terminal it will also display a progress bar:
$ dnadna simulation run my_dataset/my_dataset_simulation_config.yml ... INFO; Running one_event simulator with n_scenarios=20 and n_replicates=2 ... INFO; Simulation complete! ... INFO; Initialize model training with the command: ... INFO; ... INFO; dnadna init --simulation-config=my_dataset/my_dataset_simulation_config.yml <model-name>
The main command for initialize DNADNA is
dnadna init, which assumes we
already have a simulation (such as the one we just generated) in the
standard DNADNA Data Format. Although this command can be
run without any arguments (producing a default config file), if we pass it
the path to our simulation config file it will output a config file
appropriate for use with that simulation:
$ dnadna init --simulation-config=my_dataset/my_dataset_simulation_config.yml my_model Writing sample preprocessing config to my_model/my_model_preprocessing_config.yml ... Edit the dataset and/or preprocessing config files as needed, then run preprocessing with the command: dnadna preprocess my_model/my_model_preprocessing_config.yml
dnadna init, it is expected that the user will manually edit
the sample config file that it outputs, in order to exactly specify how they
want to train their model, and on which parameters. In fact, the default
template is going to be good enough for our demo simulation, except for one
bit that will give us trouble.
dataset_splits: has a default value meaning 70% of our
scenarios will be used for training, and only 30% for validation. Since,
for this quick demo, we only have 20 scenarios, the validation set will be
too small. Open the file
my_model/my_model_preprocessing_config.yml in your
editor and change this so that our dataset is split 50/50 between training
# my_model/my_model_preprocessing_config.yml # ... dataset_splits: training: 0.5 validation: 0.5
Under normal use you would set these ratios however you prefer. You can
also include a
test set of scenarios to be set aside for testing your
Before training a model, some data pre-processing must be performed on the
data set; the output of this pre-processing can depend on the settings in
the preprocessing config file that was output by
dnadna init. To do this,
$ dnadna preprocess my_model/my_model_preprocessing_config.yml ... INFO; Removing scenarios with: ... INFO; - Missing replicates ... INFO; - Fewer than 500 SNPs ... INFO; ... ... INFO; Using ... CPU for checking scenarios ... INFO; 20 scenarios out of 20 have been kept, representing 40 simulations ... INFO; Splitting scenarios between training and validation set ... INFO; Standardizing continuous parameters ... INFO; Writing preprocessed scenario parameters to: .../my_model/my_model_preprocessed_params.csv ... INFO; Writing sample training config to: .../my_model/my_model_training_config.yml ... INFO; Edit the training config file as needed, then start the training run with the command: ... INFO; ... INFO; dnadna train .../my_model/my_model_training_config.yml
This will produce a
<model_name>_training_config.yml file containing the
config file prepared for training your model.
To run a model training, after pre-processing use
dnadna train, giving it
the path to the pre-processed training config file as output by the last
In order to make the training run a little faster (just for this example) let’s also edit the training config file to limit it to one epoch:
# my_model/my_model_training_config.yml # ... # name and parameters of the neural net model to train network: name: CustomCNN # number of epochs over which to repeat the training process n_epochs: 1 ...
dnadna train on the training config file:
$ dnadna train my_model/my_model_training_config.yml ... INFO; Preparing training run ... INFO; 20 samples in the validation set and 20 in the training set ... INFO; Start training ... INFO; Networks states are saved after each validation step ... INFO; Starting Epoch #1 ... INFO; Validation at epoch: 1 and batch: 1 ... INFO; Compute all outputs for validation dataset... ... INFO; Done ... INFO; training loss = 1.0222865343093872 // validation loss = 1.2975229024887085 ... INFO; Better loss found on validation set: None --> 1.2975229024887085 ... INFO; Saving model to ".../my_model/run_000/my_model_run_000_best_net.pth" ... ... INFO; Compute all outputs for validation dataset... ... INFO; Done ... INFO; --- 3.185938596725464 seconds --- ... INFO; --- Best loss: 3.892427444458008 ... INFO; Saving model to ".../my_model/run_000/my_model_run_000_last_epoch_net.pth" ... ... INFO; You can test the model's predictions on a test dataset by running the command: ... INFO; ... INFO; dnadna predict .../my_model/run_000/my_model_run_000_best_net.pth <dataset config file or paths to .npz files>
By default this will output a directory for your training run under
NNN is an integer run ID. The run ID starts at
0, and by default the next unused run ID is used. However, you may also
--run-id argument to give a custom run ID, which may be either an
integer, or an arbitrary string.
Following a successful training run will output a
<model_name>_run_<run_id>_last_epoch_net.pth file in the run directory,
containing the final trained model in a pickled format, which can be loaded
Under the run directory this will also produce a
<model_name>_<run_id>_training_config.yml file containing the final config
file prepared for this training run. This contains a complete copy of the
“base” training config use used to run
dnadna train as well as a
complete copy of the simulation config. This information is copied in full
for the purpose of provenance and reproducibility of a training run.
The expectation is that between multiple training runs, you may modify the “base” config to tune the training, either by modifying the original config file directly, or by copying it and editing the copy. In any case, the final configuration used to perform the training run is saved in the run directory and should not be modified.
You can keep track of all training and validation losses with TensorBoard:
$ tensorboard --logdir my_model/
Given the trained network, we can now use it to make (or confirm)
predictions on new datasets. To demonstrate we’ll run the
model over part of the existing dataset we just used to train the model,
though in practice it could be run on any data that conforms (e.g. in
dimensions) to the dataset the model was trained on. The output is a CSV
file containing the parameter predictions for each input:
$ dnadna predict my_model/run_000/my_model_run_000_last_epoch_net.pth \ my_dataset/scenario_04/*.npz ... INFO; The following transform list will be applied to the data: training: Compose([Crop(max_snp=500, max_indiv=None, keep_polymorphic_only=True)]) path,event_time,recent_size,event_size .../my_dataset/scenario_04/my_dataset_04_0.npz,-0.06392800807952881,-0.11097482591867447,-0.12720556557178497 .../my_dataset/scenario_04/my_dataset_04_1.npz,-0.06392764300107956,-0.11097370833158493,-0.12720371782779694
See the prediction documentation for details on how to apply different transformation to the target dataset.
DNADNA has a prescribed filesystem layout and file format for the datasets its works on. Some of the details of this layout can be modified in the configuration files, and in a future version will be further customizable by plugins.
But the default format assumes that SNP data (SNP matrices and associated SNP position arrays) are stored in NumPy’s NPZ format with one file per SNP. They are organized on disk by scenario like:
\_ my_simulation/ \_ my_simulation_params.csv # the scenario parameters table |_ my_simulation_dataset_config.yml # the simulation config file |_ scenario_000/ \_ my_simulation_000_00.npz # scenario 0 replicate 0 ... |_ my_simulation_000_NN.npz |_ scenario_001/ \_ my_simulation_001_00.npz # scenario 1 replicate 0 ... |_ my_simulation_001_NN.npz ... |_ scenario_NNN/ \_ my_simulation_NNN_00.npz ... |_ my_simulation_NNN_NN.npz
my_simulation_params.csv contains the known target values for
each parameter of the simulation, on a per-scenario basis. It is currently
a plain CSV file which must contain at a minimum 3 columns:
scenario_idxcolumn giving the scenario number.
n_replicatescolumn which specifies the number of replicates in that scenario.
One or more additional columns containing arbitrary parameter names, and their values each scenario in the dataset.
scenario_idx,mutation_rate,recombination_rate,event_time,n_replicates 0,1e-08,1e-08,0.3865300203456797,-0.497464473948751,100 1,1e-08,1e-08,0.19344551118300793,0.16419897912977574,100 ...
An associated config file here named
provides further details about how to load the dataset to the DNADNA
software. An example dataset config can be generated by running
See The DNADNA Dataset Format for more details.
See the development documentation for full details on how to set up and use a development environment and contribute to DNADNA.
For the full usage manual see the DNADNA Documentation.