diff --git a/dashboard/README.md b/dashboard/README.md
index 613606a..eabca09 100644
--- a/dashboard/README.md
+++ b/dashboard/README.md
@@ -1,121 +1,104 @@
-## Dashboard
-
-Here are a few how-to guides on how to develop and use the dashboard.
-
-### Prerequisites
-- Ensure you have Conda installed.
-- Ensure you have Docker installed (if you plan to use Docker).
-
-### How to create a new conda environment lock file
-
-1. Activate the `base` conda environment:
-    ```console
-    conda activate base
-    ```
-
-2. Install `conda-lock` (if not already installed):
-    ```console
-    conda install -c conda-forge conda-lock
-    ```
-
-3. Create the lock file starting from the existing minimal environment file:
-    ```console
-    conda-lock --file environment.yml --lockfile environment-lock.yml
-    ```
-
-### How to set up the conda environment
-
-1. Activate the `base` conda environment:
-    ```console
-    conda activate base
-    ```
-
-2. Install `conda-lock` (if not already installed):
-    ```console
-    conda install -c conda-forge conda-lock
-    ```
-
-3. Create the `synapse-gui` conda environment from the lock file:
-    ```console
-    conda-lock install --name synapse-gui environment-lock.yml
-    ```
-
-### How to run the GUI
-
-1. Activate the `synapse-gui` conda environment:
-    ```console
-    conda activate synapse-gui
-    ```
-
-2. Set the database settings (read only):
-    ```console
-    export SF_DB_HOST='127.0.0.1'
-    export SF_DB_READONLY_PASSWORD='your_password_here'  # Use SINGLE quotes around the password!
-    ```
-
-3. For local development, open a separate terminal and keep it open while SSH forwarding the database connection:
-    ```console
-    ssh -L 27017:mongodb05.nersc.gov:27017 <username>@dtn03.nersc.gov -N
-    ```
-
-4. Run the GUI from the `dashboard/` folder:
-    - Via the web browser interface:
-    ```console
-    python -u app.py --port 8080
-    ```
-    - As a desktop application:
-    ```console
-    python -u app.py --app
-    ```
-    If you run the GUI as a desktop application, make sure to set the following environment variable first:
-    ```console
-    python -m pip install pywebview[qt]
-    export PYWEBVIEW_GUI=qt
-    ```
-
-5. Terminate the GUI via `Ctrl` + `C`.
-
-### How to build and run the Docker container
+# Table of Contents
+* [Overview](#Overview)
+* [Run the Dashboard Locally](#Run-the-Dashboard-Locally)
+    * [Without Docker](#Without-Docker)
+    * [With Docker](#With-Docker)
+* [Run the Dashboard at NERSC](#Run-the-Dashboard-at-NERSC)
+* [Get the Superfacility API Credentials](#Get-the-Superfacility-API-Credentials)
+* [For Maintainers](#For-Maintainers)
+    * [Generate the conda environment lock file](#Generate-the-conda-environment-lock-file)
+    * [Build and push the Docker container to NERSC](#Build-and-push-the-Docker-container-to-NERSC)
+* [References](#References)
 
-1. Move to the root directory of the repository.
+# Overview
+
+The Synapse dashboard provides a web interface for working with data from experiments, simulations, and ML models.
+
+The dashboard can be run in two distinct ways:
+
+1. Locally on your computer.
+
+2. At NERSC through Spin.
+
+# Run the Dashboard Locally
+
+This section describes how to develop and use the dashboard locally.
+
+## Without Docker
+
+### Prepare the conda environment
+
+1. Move to the [dashboard/](./) directory.
+
+2. Activate the conda environment `base`:
+```bash
+conda activate base
+```
+
+3. Install `conda-lock` if not installed yet:
+```bash
+conda install -c conda-forge conda-lock
+```
+
+4. Create the conda environment `synapse-gui`:
+```bash
+conda-lock install --name synapse-gui environment-lock.yml
+```
+
+### Run the dashboard
+
+1. Create an SSH tunnel to access the MongoDB database at NERSC (in a separate terminal):
+   ```bash
+   ssh -L 27017:mongodb05.nersc.gov:27017 <username>@dtn03.nersc.gov -N
+   ```
+
+2. Move to the [dashboard/](./) directory.
+
+3. Set up the database settings (read-only):
+   ```bash
+   export SF_DB_HOST='127.0.0.1'
+   export SF_DB_READONLY_PASSWORD='your_password_here'  # Use SINGLE quotes around the password!
+   ```
+
+4. Activate the conda environment `synapse-gui`:
+   ```bash
+   conda activate synapse-gui
+   ```
+
+5. Run the dashboard as a web application:
+   ```bash
+   python -u app.py --port 8080
+   ```
+
+## With Docker
+
+### Run the dashboard
+
+1. Create an SSH tunnel to access the MongoDB database at NERSC (in a separate terminal):
+   ```bash
+   ssh -L 27017:mongodb05.nersc.gov:27017 <username>@dtn03.nersc.gov -N
+   ```
+
+2. Move to the root directory of the repository.
+
+3. Build the Docker image as described [below](#build-the-docker-image).
+
+4. Run the Docker container:
+   ```bash
+   docker run --network=host -v /etc/localtime:/etc/localtime -v $PWD/ml:/app/ml -e SF_DB_HOST='127.0.0.1' -e SF_DB_READONLY_PASSWORD='your_password_here' synapse-gui
+   ```
+   For debugging, you can enter the container without starting the app:
+   ```bash
+   docker run --network=host -v /etc/localtime:/etc/localtime -v $PWD/ml:/app/ml -e SF_DB_HOST='127.0.0.1' -e SF_DB_READONLY_PASSWORD='your_password_here' -it synapse-gui bash
+   ```
+   Note that `-v /etc/localtime:/etc/localtime` is necessary to synchronize the time zone in the container with the host machine.
 
-2. Build the Docker image based on `Dockerfile`:
-    ```console
-    docker build --platform linux/amd64 -t synapse-gui -f dashboard.Dockerfile .
-    ```
-
-3. Run the Docker container from the `dashboard/` folder:
-    ```console
-    docker run --network=host -v /etc/localtime:/etc/localtime -v $PWD/ml:/app/ml -e SF_DB_HOST='127.0.0.1' -e SF_DB_READONLY_PASSWORD='your_password_here' synapse-gui
-    ```
-    For debugging, you can also enter the container without starting the app:
-    ```console
-    docker run --network=host -v /etc/localtime:/etc/localtime -v $PWD/ml:/app/ml -e SF_DB_HOST='127.0.0.1' -e SF_DB_READONLY_PASSWORD='your_password_here' -it synapse-gui bash
-    ```
-    Note that `-v /etc/localtime:/etc/localtime` is necessary to synchronize the time zone in the container with the host machine.
-
-4. Optional: Publish the container privately to NERSC registry (https://registry.nersc.gov):
-    ```console
-    docker login registry.nersc.gov
-    # Username: your NERSC username
-    # Password: your NERSC password without 2FA
-    ```
-    ```console
-    docker tag synapse-gui:latest registry.nersc.gov/m558/superfacility/synapse-gui:latest
-    docker tag synapse-gui:latest registry.nersc.gov/m558/superfacility/synapse-gui:$(date "+%y.%m")
-    docker push -a registry.nersc.gov/m558/superfacility/synapse-gui
-    ```
-    This has been also automated through the Python script [publish_container.py](https://github.com/BLAST-AI-ML/synapse/blob/main/publish_container.py), which can be executed via
-    ```console
-    python publish_container.py --gui
-    ```
-
-5. Optional: From time to time, as you develop the container, you might want to prune old, unused images to get back GBytes of storage on your development machine:
-    ```console
-    docker system prune -a
-    ```
-
-### How to get the Superfacility API credentials
+# Run the Dashboard at NERSC
+
+Connect to the [dashboard](https://bellasuperfacility.lbl.gov/) deployed at NERSC through Spin and play around!
+Remember that you need to upload valid Superfacility API credentials in order to launch simulations or train ML models directly from the dashboard.
+
+# Get the Superfacility API Credentials
 
 Following the instructions at [docs.nersc.gov/services/sfapi/authentication/#client](https://docs.nersc.gov/services/sfapi/authentication/#client):
 
@@ -127,15 +110,89 @@ Following the instructions at [docs.nersc.gov/services/sfapi/authentication/#cli
 
 4. Enter a client name (e.g., "Synapse"), choose `sf558` for the user, choose "Red" security level, and select either "Your IP" or "Spin" from the "IP Presets" menu, depending on whether the key will be used from a local computer or from Spin.
 
-5. Download the private key file (in pem format) and save it as `priv_key.pem` in the root directory of the GUI.
-   Each time the GUI is launched, it will automatically find the existing key file and load the corresponding credentials.
+5. Download the private key file (in pem format) and save it as `priv_key.pem` in the root directory of the dashboard.
+   Each time the dashboard is launched, it will automatically find the existing key file and load the corresponding credentials.
 
 6. Copy your client ID and add it on the first line of your private key file as described in the instructions at [nersc.github.io/sfapi_client/quickstart/#storing-keys-in-files](https://nersc.github.io/sfapi_client/quickstart/#storing-keys-in-files):
-    ```
-    randmstrgz
-    -----BEGIN RSA PRIVATE KEY-----
-    ...
-    -----END RSA PRIVATE KEY-----
-    ```
+   ```
+   randmstrgz
+   -----BEGIN RSA PRIVATE KEY-----
+   ...
+   -----END RSA PRIVATE KEY-----
+   ```
 
 7. Run `chmod 600 priv_key.pem` to change the permissions of your private key file to read/write only.
+
+# For Maintainers
+
+## Generate the conda environment lock file
+
+1. Move to the directory [dashboard/](.).
+
+2. Activate the conda environment `base`:
+   ```bash
+   conda activate base
+   ```
+
+3. Install `conda-lock` if not installed yet:
+   ```bash
+   conda install -c conda-forge conda-lock
+   ```
+
+4. Generate the conda environment lock file:
+   ```bash
+   conda-lock --file environment.yml --lockfile environment-lock.yml
+   ```
+
+## Build and push the Docker container to NERSC
+
+> [!WARNING]
+> Pushing a new Docker container affects the production dashboard deployed at NERSC through Spin.
+
+> [!TIP]
+> Run this workflow automatically with the Python script [publish_container.py](../publish_container.py):
+> ```bash
+> python publish_container.py --gui
+> ```
+
+> [!TIP]
+> Prune old, unused images periodically in order to free up space on your machine:
+> ```bash
+> docker system prune -a
+> ```
+
+### Build the Docker image
+
+1. Move to the root directory of the repository.
+
+2. Build the Docker image:
+   ```bash
+   docker build --platform linux/amd64 -t synapse-gui -f dashboard.Dockerfile .
+   ```
+
+### Push the Docker container
+
+1. Move to the root directory of the repository.
+
+2. Login to the [NERSC registry](https://registry.nersc.gov):
+   ```bash
+   docker login registry.nersc.gov
+   # Username: your NERSC username
+   # Password: your NERSC password without 2FA
+   ```
+
+3. Tag the Docker image:
+   ```bash
+   docker tag synapse-gui:latest registry.nersc.gov/m558/superfacility/synapse-gui:latest
+   docker tag synapse-gui:latest registry.nersc.gov/m558/superfacility/synapse-gui:$(date "+%y.%m")
+   ```
+
+4. Push the Docker container:
+   ```bash
+   docker push -a registry.nersc.gov/m558/superfacility/synapse-gui
+   ```
+
+# References
+
+* [Using NERSC's `registry.nersc.gov`](https://docs.nersc.gov/development/containers/registry/)
+* [Superfacility API authentication](https://docs.nersc.gov/services/sfapi/authentication/#client)
diff --git a/ml.Dockerfile b/ml.Dockerfile
index a4ba628..09392d7 100644
--- a/ml.Dockerfile
+++ b/ml.Dockerfile
@@ -10,10 +10,11 @@ ENV PIP_ROOT_USER_ACTION=ignore
 # Install any needed packages specified in the environment file
 # Match the CUDA 12.4.0 on Perlmutter (NERSC):
 #   https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#specifying-a-different-target-platform-for-an-environment
-COPY ml/environment.yml /app/ml/environment.yml
+COPY ml/environment-lock.yml /app/ml/environment-lock.yml
 ENV CONDA_OVERRIDE_CUDA=12.4.0
 RUN conda info
-RUN conda env create -y -f environment.yml \
+RUN conda install -c conda-forge conda-lock \
+    && conda-lock install --name synapse-ml environment-lock.yml \
     && conda clean --all -y
 
 # Configure an exectuable entrypoint script
diff --git a/ml/README.md b/ml/README.md
index 6c04e8f..d55a5e1 100644
--- a/ml/README.md
+++ b/ml/README.md
@@ -1,133 +1,237 @@
-# ML Training
+# Table of Contents
+* [Overview](#Overview)
+* [Train ML Models Locally](#Train-ML-Models-Locally)
+    * [Without Docker](#Without-Docker)
+    * [With Docker](#With-Docker)
+* [Train ML Models at NERSC](#Train-ML-Models-at-NERSC)
+    * [Manually without Docker](#Manually-without-Docker)
+    * [Manually with Docker](#Manually-with-Docker)
+    * [Through the dashboard](#Through-the-dashboard)
+* [For Maintainers](#For-Maintainers)
+    * [Generate the conda environment lock file](#Generate-the-conda-environment-lock-file)
+    * [Build and push the Docker container to NERSC](#Build-and-push-the-Docker-container-to-NERSC)
+* [References](#References)
 
-The ML training (implemented in ``train_model.py``) can be run in two ways:
+# Overview
 
-- In your local Python environment, for testing/debugging: ``python train_model.py ...``
+Synapse's ML training is implemented primarily in [train_model.py](train_model.py).
 
-- Through the GUI, by clicking the ``Train`` button, or through SLURM by running ``sbatch training_pm.sbatch``.
-In both cases, the training runs in a Docker container at NERSC. This Docker container
-is pulled from the NERSC registry (https://registry.nersc.gov) and does not reflect any local changes
-you may have made to ``train_model.py``, unless you re-build and re-deploy the container.
+ML models can be trained in two distinct ways:
 
-Both methods are described in more detail below.
+1. Locally on your computer.
 
-## Training in a local Python environment (testing/debugging)
+2. At NERSC, either manually or through the dashboard.
 
-### On your local computer
+# Train ML Models Locally
 
-For local development, ensure you have [Conda](https://conda-forge.org/download/) installed. Then:
+This section describes how to train ML models locally.
 
-1. Create the conda environment (this only needs to be done once):
+## Without Docker
+
+### Prepare the conda environment
+
+1. Move to the [ml/](./) directory.
+
+2. Activate the conda environment `base`:
    ```bash
-   conda env create -f environment.yml
+   conda activate base
    ```
 
-2. Open a separate terminal and keep it open:
+3. Install `conda-lock` if not installed yet:
+   ```bash
+   conda install -c conda-forge conda-lock
+   ```
+
+4. Create the conda environment `synapse-ml`:
+   ```bash
+   conda-lock install --name synapse-ml environment-lock.yml
+   ```
+
+### Run the training
+
+1. Create an SSH tunnel to access the MongoDB database at NERSC (in a separate terminal):
    ```bash
    ssh -L 27017:mongodb05.nersc.gov:27017 <username>@dtn03.nersc.gov -N
    ```
 
-3. Activate the conda environment and setup database read-write access:
+2. Move to the [ml/](./) directory.
+
+3. Set up the database settings (read-write):
    ```bash
-   conda activate synapse-ml
    export SF_DB_ADMIN_PASSWORD='your_password_here'  # Use SINGLE quotes around the password!
    ```
 
-4. Run the training script in test mode:
-   ```console
-   python train_model.py --test --model <NN/GP> --config_file <your_test_yaml_file>
+4. Activate the conda environment `synapse-ml`:
+   ```bash
+   conda activate synapse-ml
    ```
 
-### At NERSC
-
-1. Create the conda environment (this only needs to be done once):
+5. Run the ML training script in test mode:
    ```bash
-   module load python
-   conda env create --prefix /global/cfs/cdirs/m558/$(whoami)/sw/perlmutter/synapse-ml -f environment.yml
+   python train_model.py --test --model <your_model> --config_file <your_config_file>
    ```
 
-2. Activate the environment and setup database read-write access:
+## With Docker
+
+Coming soon.
+
+# Train ML Models at NERSC
+
+This section describes how to train ML models at NERSC.
+
+## Manually without Docker
+
+### Prepare the conda environment
+
+1. Move to the [ml/](./) directory.
+
+2. Activate your own user base conda environment:
    ```bash
    module load python
-   conda activate /global/cfs/cdirs/m558/$(whoami)/sw/perlmutter/synapse-ml
-   export SF_DB_ADMIN_PASSWORD='your_password_here'  # Use SINGLE quotes around the password!
+   conda activate <your_base_env>
    ```
 
-3. Run the training script in test mode:
-   ```console
-   python train_model.py --test --model <NN/GP> --config_file <your_test_yaml_file>
+3. Install `conda-lock` if not installed yet:
+   ```bash
+   conda install -c conda-forge conda-lock
    ```
 
-## Training through the GUI or through SLURM
+4. Create the conda environment `synapse-ml`:
+   ```bash
+   conda-lock install --name synapse-ml environment-lock.yml
+   ```
 
-> **Warning:**
->
-> Pushing a new Docker container affects training jobs launched from your locally-deployed GUI,
-> but also from the production GUI (deployed on NERSC Spin), since in both cases, the training
-> runs in a Docker container at NERSC, which is pulled from the NERSC registry (https://registry.nersc.gov).
->
-> Yet, currently, this is the only way to test the end-to-end integration of the GUI with the training workflow.
+### Run the training
 
-1. Move to the root directory of the repository.
+1. Move to the [ml/](./) directory.
 
-2. Build the Docker image based on `Dockerfile`:
-   ```console
-   docker build --platform linux/amd64 -t synapse-ml -f ml.Dockerfile .
+2. Set up the database settings (read-write):
+   ```bash
+   export SF_DB_ADMIN_PASSWORD='your_password_here'  # Use SINGLE quotes around the password!
    ```
 
-3. Optional: From time to time, as you develop the container, you might want to prune old, unused images to get back GBytes of storage on your development machine:
-   ```console
-   docker system prune -a
+3. Activate the conda environment `synapse-ml`:
+   ```bash
+   module load python
+   conda activate synapse-ml
    ```
 
-4. Publish the container privately to NERSC registry (https://registry.nersc.gov):
-   ```console
-   docker login registry.nersc.gov
-   # Username: your NERSC username
-   # Password: your NERSC password without 2FA
+4. Run the ML training script in test mode:
+   ```bash
+   python train_model.py --test --model <your_model> --config_file <your_config_file>
    ```
 
-   ```console
-   docker tag synapse-ml:latest registry.nersc.gov/m558/superfacility/synapse-ml:latest
-   docker tag synapse-ml:latest registry.nersc.gov/m558/superfacility/synapse-ml:$(date "+%y.%m")
-   docker push -a registry.nersc.gov/m558/superfacility/synapse-ml
-   ```
-    This has been also automated through the Python script [publish_container.py](https://github.com/BLAST-AI-ML/synapse/blob/main/publish_container.py), which can be executed via
-    ```console
-    python publish_container.py --ml
-    ```
+## Manually with Docker
+
+> [!WARNING]
+> Note that the Docker container is pulled from the [NERSC registry](https://registry.nersc.gov) and does not reflect any local changes you may have made to [train_model.py](train_model.py), unless you re-build and re-deploy the container first.
 
-5. Optional test: Run the Docker container manually on Perlmutter:
-   ```console
+1. Log in to Perlmutter:
+   ```bash
    ssh perlmutter-p1.nersc.gov
+   ```
+
+2. Ensure the file `$HOME/db.profile` contains the line `export SF_DB_ADMIN_PASSWORD='your_password_here'` with the read-write password to the database.
 
+3. Pull the Docker container:
+   ```bash
    podman-hpc login --username $USER registry.nersc.gov
    # Password: your NERSC password without 2FA
-
    podman-hpc pull registry.nersc.gov/m558/superfacility/synapse-ml:latest
    ```
 
-   Ensure the file `$HOME/db.profile` contains a line `export SF_DB_ADMIN_PASSWORD=...` with the write password to the database.
-
-   ```console
+4. Allocate a GPU node and run the container:
+   ```bash
    salloc -N 1 --ntasks-per-node=1 -t 1:00:00 -q interactive -C gpu --gpu-bind=single:1 -c 32 -G 1 -A m558
-
    podman-hpc run --gpu -v /etc/localtime:/etc/localtime -v $HOME/db.profile:/root/db.profile -v /path/to/config.yaml:/app/ml/config.yaml --rm -it registry.nersc.gov/m558/superfacility/synapse-ml:latest python -u /app/ml/train_model.py --test --config_file /app/ml/config.yaml --model NN
    ```
    Note that `-v /etc/localtime:/etc/localtime` is necessary to synchronize the time zone in the container with the host machine.
 
+## Through the dashboard
 
-> **Note:**
->
-> When we run ML training jobs through the GUI, we use NERSC's Superfacility API with the collaboration account `sf558`.
-> Since this is a non-interactive, non-user account, we also use a custom user to pull the image from https://registry.nersc.gov to Perlmutter.
-> The registry login credentials need to be prepared (once) in the `$HOME` of `sf558` (`/global/homes/s/sf558/`) in a file named `registry.profile` with the following content:
+> [!WARNING]
+> When we train ML models through the dashboard, we use NERSC's Superfacility API with the collaboration account `sf558`.
+> Since this is a non-interactive, non-user account, we also use a custom user to pull the image from the [NERSC registry](https://registry.nersc.gov) to Perlmutter.
+> The registry login credentials need to be prepared (only once) in the `$HOME` of user `sf558` (`/global/homes/s/sf558/`), in a file named `registry.profile` with the following content:
 > ```bash
 > export REGISTRY_USER="robot\$m558+perlmutter-nersc-gov"
 > export REGISTRY_PASSWORD="..."
 > ```
 
-## References
+Connect to the [dashboard](https://bellasuperfacility.lbl.gov/) deployed at NERSC through Spin and click the `Train` button in the `ML` panel.
+Remember that you need to upload valid Superfacility API credentials in order to launch simulations or train ML models directly from the dashboard.
+
+# For Maintainers
+
+## Generate the conda environment lock file
+
+1. Move to the directory [ml/](./).
+
+2. Activate the conda environment `base`:
+   ```bash
+   conda activate base
+   ```
+
+3. Install `conda-lock` if not installed yet:
+   ```bash
+   conda install -c conda-forge conda-lock
+   ```
+
+4. Generate the conda environment lock file:
+   ```bash
+   conda-lock --file environment.yml --virtual-package-spec virtual-packages.yml --lockfile environment-lock.yml
+   ```
+
+## Build and push the Docker container to NERSC
+
+> [!WARNING]
+> Pushing a new Docker container affects both the ML training jobs launched from a dashboard deployed locally and the ML training jobs launched from the dashboard deployed at NERSC, because in both cases the ML training runs in a Docker container pulled from the [NERSC registry](https://registry.nersc.gov).
+> Currently, this is the only way to test the end-to-end integration of the dashboard with the ML training workflow.
+
+> [!TIP]
+> Run this workflow automatically with the Python script [publish_container.py](../publish_container.py):
+> ```bash
+> python publish_container.py --ml
+> ```
+
+> [!TIP]
+> Prune old, unused images periodically in order to free up space on your machine:
+> ```bash
+> docker system prune -a
+> ```
+
+### Build the Docker image
+
+1. Move to the root directory of the repository.
+
+2. Build the Docker image:
+   ```bash
+   docker build --platform linux/amd64 -t synapse-ml -f ml.Dockerfile .
+   ```
+
+### Push the Docker container
+
+1. Move to the root directory of the repository.
+
+2. Login to the [NERSC registry](https://registry.nersc.gov):
+   ```bash
+   docker login registry.nersc.gov
+   # Username: your NERSC username
+   # Password: your NERSC password without 2FA
+   ```
+
+3. Tag the Docker image:
+   ```bash
+   docker tag synapse-ml:latest registry.nersc.gov/m558/superfacility/synapse-ml:latest
+   docker tag synapse-ml:latest registry.nersc.gov/m558/superfacility/synapse-ml:$(date "+%y.%m")
+   ```
+
+4. Push the Docker container:
+   ```bash
+   docker push -a registry.nersc.gov/m558/superfacility/synapse-ml
+   ```
+
+# References
 
-* https://docs.nersc.gov/development/containers/podman-hpc/overview/
-* https://docs.nersc.gov/development/containers/registry/
+* [Using NERSC's `registry.nersc.gov`](https://docs.nersc.gov/development/containers/registry/)
+* [Podman at NERSC](https://docs.nersc.gov/development/containers/podman-hpc/overview/)
diff --git a/ml/environment-lock.yml b/ml/environment-lock.yml
new file mode 100644
index 0000000..31b36a7
--- /dev/null
+++ b/ml/environment-lock.yml
@@ -0,0 +1,16920 @@
+# This lock file was generated by conda-lock (https://github.com/conda/conda-lock). DO NOT EDIT!
+#
+# A "lock file" contains a concrete list of package versions (with checksums) to be installed. Unlike
+# e.g. `conda env create`, the resulting environment will not change as new package versions become
+# available, unless you explicitly update the lock file.
+#
+# Install this environment as "YOURENV" with:
+#     conda-lock install -n YOURENV environment-lock.yml
+# To update a single package to the latest version compatible with the version constraints in the source:
+#     conda-lock lock  --lockfile environment-lock.yml --update PACKAGE
+# To re-solve the entire environment, e.g. after changing a version constraint in the source file:
+#     conda-lock -f environment.yml --lockfile environment-lock.yml
+version: 1
+metadata:
+  content_hash:
+    linux-64: 81b1254687611f89b40960559c4cf6136a2b63d070bcec452c190a455f267007
+    osx-arm64: 2e3144d3f24ea1728f13acb0cd2a8f27cced4b702484d8336776f9a26b4a1c1d
+    osx-64: fc32ee6fa37cfbd9d6d76681ea639d0802abc2cab632711c75e2d87e6b0abdd4
+    win-64: 40413f4bb063478164d92dddb7af269c5363809dc2c27b89bbf88a9cd6c97a0b
+  channels:
+  - url: conda-forge
+    used_env_vars: []
+  platforms:
+  - linux-64
+  - osx-64
+  - osx-arm64
+  - win-64
+  sources:
+  - environment.yml
+package:
+- name: _openmp_mutex
+  version: '4.5'
+  manager: conda
+  platform: linux-64
+  dependencies:
+    llvm-openmp: '>=9.0.1'
+  url: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda
+  hash:
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+    sha256: c0cddb66070dd6355311f7667ce2acccf70d1013edaa6e97f22859502fefdb22
+  category: main
+  optional: false
+- name: _openmp_mutex
+  version: '4.5'
+  manager: conda
+  platform: osx-64
+  dependencies:
+    llvm-openmp: '>=9.0.1'
+  url: https://conda.anaconda.org/conda-forge/osx-64/_openmp_mutex-4.5-7_kmp_llvm.conda
+  hash:
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+    sha256: 30006902a9274de8abdad5a9f02ef7c8bb3d69a503486af0c1faee30b023e5b7
+  category: main
+  optional: false
+- name: _openmp_mutex
+  version: '4.5'
+  manager: conda
+  platform: osx-arm64
+  dependencies:
+    llvm-openmp: '>=9.0.1'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/_openmp_mutex-4.5-7_kmp_llvm.conda
+  hash:
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+  category: main
+  optional: false
+- name: _openmp_mutex
+  version: '4.5'
+  manager: conda
+  platform: win-64
+  dependencies:
+    libgomp: '>=7.5.0'
+    libwinpthread: '>=12.0.0.r2.ggc561118da'
+  url: https://conda.anaconda.org/conda-forge/win-64/_openmp_mutex-4.5-2_gnu.conda
+  hash:
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+    sha256: 1a62cd1f215fe0902e7004089693a78347a30ad687781dfda2289cab000e652d
+  category: main
+  optional: false
+- name: aiohappyeyeballs
+  version: 2.6.1
+  manager: conda
+  platform: linux-64
+  dependencies:
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+  url: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.6.1-pyhd8ed1ab_0.conda
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+  optional: false
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+  platform: osx-64
+  dependencies:
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+  dependencies:
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+  url: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.6.1-pyhd8ed1ab_0.conda
+  hash:
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+    sha256: 7842ddc678e77868ba7b92a726b437575b23aaec293bca0d40826f1026d90e27
+  category: main
+  optional: false
+- name: aiohappyeyeballs
+  version: 2.6.1
+  manager: conda
+  platform: win-64
+  dependencies:
+    python: '>=3.9'
+  url: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.6.1-pyhd8ed1ab_0.conda
+  hash:
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+  category: main
+  optional: false
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+  manager: conda
+  platform: linux-64
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+    libgcc: '>=14'
+    multidict: '>=4.5,<7.0'
+    propcache: '>=0.2.0'
+    python: '>=3.11,<3.12.0a0'
+    python_abi: 3.11.*
+    yarl: '>=1.17.0,<2.0'
+  url: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.13.3-py311h55b9665_0.conda
+  hash:
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+    sha256: 6ba089f4030fdf139acae5fbf6d20907c53f8506110ec9ab242dcf6efa18f267
+  category: main
+  optional: false
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+  hash:
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+    propcache: '>=0.2.0'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
+    yarl: '>=1.17.0,<2.0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aiohttp-3.13.3-py312h9f8c436_0.conda
+  hash:
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+  category: main
+  optional: false
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+  manager: conda
+  platform: win-64
+  dependencies:
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+  hash:
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+  platform: win-64
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+    typing-extensions: '>=4.0.0'
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+  platform: linux-64
+  dependencies:
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+    idna: '>=2.8'
+    python: ''
+    typing_extensions: '>=4.5'
+  url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.12.1-pyhcf101f3_0.conda
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+  dependencies:
+    __osx: '>=11.0'
+    idna: '>=2.0'
+    multidict: '>=4.0'
+    propcache: '>=0.2.1'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/yarl-1.22.0-py312h5748b74_0.conda
+  hash:
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+    sha256: 49ee6fcb59e63cceb1f01777ac8b67d44633b6cdad5c47b02bc995f6e96955eb
+  category: main
+  optional: false
+- name: yarl
+  version: 1.22.0
+  manager: conda
+  platform: win-64
+  dependencies:
+    idna: '>=2.0'
+    multidict: '>=4.0'
+    propcache: '>=0.2.1'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
+    ucrt: '>=10.0.20348.0'
+    vc: '>=14.3,<15'
+    vc14_runtime: '>=14.44.35208'
+  url: https://conda.anaconda.org/conda-forge/win-64/yarl-1.22.0-py312h05f76fc_0.conda
+  hash:
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+    sha256: b622ef03b033a1c3984cb3e47e198370f23bf239c579a0c04f9179237fbb541b
+  category: main
+  optional: false
+- name: zipp
+  version: 3.23.0
+  manager: conda
+  platform: linux-64
+  dependencies:
+    python: ''
+  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
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+  optional: false
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+  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
+  hash:
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+  category: main
+  optional: false
+- name: zipp
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+  manager: conda
+  platform: osx-arm64
+  dependencies:
+    python: '>=3.10'
+  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
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+  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
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+  platform: osx-64
+  dependencies:
+    __osx: '>=10.13'
+    libzlib: 1.3.1
+  url: https://conda.anaconda.org/conda-forge/osx-64/zlib-1.3.1-hd23fc13_2.conda
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+  optional: false
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+  platform: osx-arm64
+  dependencies:
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+    libzlib: 1.3.1
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda
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+  manager: conda
+  platform: linux-64
+  dependencies:
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+    libgcc: '>=14'
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+  url: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.3-hceb46e0_1.conda
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+  optional: false
+- name: zlib-ng
+  version: 2.3.3
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+  platform: osx-64
+  dependencies:
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+    libcxx: '>=19'
+  url: https://conda.anaconda.org/conda-forge/osx-64/zlib-ng-2.3.3-h8bce59a_1.conda
+  hash:
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+  optional: false
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+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    libcxx: '>=19'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.3-hed4e4f5_1.conda
+  hash:
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+  category: main
+  optional: false
+- name: zlib-ng
+  version: 2.3.3
+  manager: conda
+  platform: win-64
+  dependencies:
+    ucrt: '>=10.0.20348.0'
+    vc: '>=14.3,<15'
+    vc14_runtime: '>=14.44.35208'
+  url: https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.3.3-h0261ad2_1.conda
+  hash:
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+    sha256: 71332532332d13b5dbe57074ddcf82ae711bdc132affa5a2982a29ffa06dc234
+  category: main
+  optional: false
+- name: zstandard
+  version: 0.25.0
+  manager: conda
+  platform: linux-64
+  dependencies:
+    __glibc: '>=2.17,<3.0.a0'
+    cffi: '>=1.11'
+    libgcc: '>=14'
+    python: ''
+    python_abi: 3.11.*
+    zstd: '>=1.5.7,<1.6.0a0'
+  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.25.0-py311haee01d2_1.conda
+  hash:
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+  category: main
+  optional: false
+- name: zstd
+  version: 1.5.7
+  manager: conda
+  platform: linux-64
+  dependencies:
+    __glibc: '>=2.17,<3.0.a0'
+    libzlib: '>=1.3.1,<2.0a0'
+  url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda
+  hash:
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+  category: main
+  optional: false
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+  manager: conda
+  platform: osx-64
+  dependencies:
+    __osx: '>=10.13'
+    libzlib: '>=1.3.1,<2.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h3eecb57_6.conda
+  hash:
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+    sha256: 47101a4055a70a4876ffc87b750ab2287b67eca793f21c8224be5e1ee6394d3f
+  category: main
+  optional: false
+- name: zstd
+  version: 1.5.7
+  manager: conda
+  platform: osx-arm64
+  dependencies:
+    __osx: '>=11.0'
+    libzlib: '>=1.3.1,<2.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda
+  hash:
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+    sha256: 9485ba49e8f47d2b597dd399e88f4802e100851b27c21d7525625b0b4025a5d9
+  category: main
+  optional: false
+- name: zstd
+  version: 1.5.7
+  manager: conda
+  platform: win-64
+  dependencies:
+    libzlib: '>=1.3.1,<2.0a0'
+    ucrt: '>=10.0.20348.0'
+    vc: '>=14.3,<15'
+    vc14_runtime: '>=14.44.35208'
+  url: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda
+  hash:
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+    sha256: 368d8628424966fd8f9c8018326a9c779e06913dd39e646cf331226acc90e5b2
+  category: main
+  optional: false
diff --git a/ml/environment.yml b/ml/environment.yml
index 11bad64..3bb30d9 100644
--- a/ml/environment.yml
+++ b/ml/environment.yml
@@ -2,17 +2,18 @@ name: synapse-ml
 
 channels:
   - conda-forge
-  - nvidia
   - nodefaults
 
 dependencies:
   - botorch
-  - cuda-version=12.4  # for Perlmutter (NERSC)
   - gpytorch
   - lume-model>=2.0.1
+  - mlflow
   - pandas
   - pip
   - pymongo
-  - python
-  - pytorch-gpu
+  - python<3.13
+  - pytorch
   - pyyaml
+  - cuda-version=12.4  # [linux]
+  - pytorch =*=cuda*  # [linux]
\ No newline at end of file
diff --git a/ml/virtual-packages.yml b/ml/virtual-packages.yml
new file mode 100644
index 0000000..dcda250
--- /dev/null
+++ b/ml/virtual-packages.yml
@@ -0,0 +1,10 @@
+# Run on Perlmutter GPU node to get these values:
+#   nvidia-smi | grep "CUDA Version"
+#   ldd --version | head -1
+#   uname -r
+subdirs:
+  linux-64:
+    packages:
+      __cuda: "12.4"
+      __glibc: "2.31"
+      __linux: "5.14"
\ No newline at end of file