Adds compatibility with NVIDIA AI Workbench

.gitattributes

@@ -0,0 +1,2 @@
+models/** filter=lfs diff=lfs merge=lfs -text
+data/** filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,57 @@
+# Ignore generated or temporary files managed by the Workbench
+.project/*
+!.project/spec.yaml
+!.project/configpacks
+
+# General ignores
+.DS_Store
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Temp directories, notebooks created by jupyterlab
+.ipynb_checkpoints
+.Trash-*/
+.jupyter/
+.local/
+
+# Python distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Workbench Project Layout
+data/scratch/*
+!data/scratch/.gitkeep

.project/configpacks

@@ -0,0 +1,10 @@
+*defaults.ContainerUser
+*bash.PreBuild
+*defaults.EnvVars
+*defaults.Readme
+*defaults.Entrypoint
+*apt.PackageManager
+*bash.PreLanguage
+*python.PipPackageManager
+*bash.PostBuild
+*jupyterlab.JupyterLab

.project/spec.yaml

@@ -0,0 +1,101 @@
+specVersion: v2
+specMinorVersion: 2
+meta:
+  name: generative-virtual-screening
+  image: generative-virtual-screening
+  description: ""
+  labels: []
+  createdOn: "2024-10-06T16:45:44Z"
+  defaultBranch: main
+layout:
+- path: src/
+  type: code
+  storage: git
+- path: deploy/
+  type: code
+  storage: git
+environment:
+  base:
+    registry: nvcr.io
+    image: nvidia/ai-workbench/python-basic:1.0.2
+    build_timestamp: "20231114175838"
+    name: Python Basic
+    supported_architectures: []
+    cuda_version: ""
+    description: A Python Base with Jupyterlab
+    entrypoint_script: ""
+    labels:
+    - ubuntu
+    - python3
+    - jupyterlab
+    apps:
+    - name: jupyterlab
+      type: jupyterlab
+      class: webapp
+      start_command: jupyter lab --allow-root --port 8888 --ip 0.0.0.0 --no-browser
+        --NotebookApp.base_url=\$PROXY_PREFIX --NotebookApp.default_url=/lab --NotebookApp.allow_origin='*'
+      health_check_command: '[ \$(echo url=\$(jupyter lab list | head -n 2 | tail
+        -n 1 | cut -f1 -d'' '' | grep -v ''Currently'' | sed "s@/?@/lab?@g") | curl
+        -o /dev/null -s -w ''%{http_code}'' --config -) == ''200'' ]'
+      stop_command: jupyter lab stop 8888
+      user_msg: ""
+      logfile_path: ""
+      timeout_seconds: 60
+      icon_url: ""
+      webapp_options:
+        autolaunch: true
+        port: "8888"
+        proxy:
+          trim_prefix: false
+        url_command: jupyter lab list | head -n 2 | tail -n 1 | cut -f1 -d' ' | grep
+          -v 'Currently'
+    programming_languages:
+    - python3
+    icon_url: ""
+    image_version: 1.0.2
+    os: linux
+    os_distro: ubuntu
+    os_distro_release: "22.04"
+    schema_version: v2
+    user_info:
+      uid: ""
+      gid: ""
+      username: ""
+    package_managers:
+    - name: apt
+      binary_path: /usr/bin/apt
+      installed_packages:
+      - curl
+      - git
+      - git-lfs
+      - python3
+      - gcc
+      - python3-dev
+      - python3-pip
+      - vim
+    - name: pip
+      binary_path: /usr/local/bin/pip
+      installed_packages:
+      - jupyterlab==4.0.7
+      - requests==2.31.0
+    package_manager_environment:
+      name: ""
+      target: ""
+execution:
+  apps: []
+  resources:
+    gpu:
+      requested: 0
+    sharedMemoryMB: 0
+  secrets:
+  - variable: NGC_CLI_API_KEY
+    description: ""
+  mounts:
+  - type: project
+    target: /project/
+    description: Project directory
+    options: rw
+  - type: volume
+    target: /nvwb-shared-volume/
+    description: ""
+    options: volumeName=nvwb-shared-volume

README.md

@@ -1,4 +1,6 @@
-<h2><img align="center" src="https://github.com/NVIDIA-BioNeMo-blueprints/generative-virtual-screening/blob/main/nvidia-logo.png?raw=true">NVIDIA BioNeMo Blueprint: Generative Virtual Screening for Drug Discovery</h2>
+<h2><img align="center" src="https://github.com/NVIDIA-NIM-Agent-Blueprints/generative-virtual-screening/blob/main/nvidia-logo.png?raw=true">NVIDIA NIM Agent Blueprints: Generative Virtual Screening for Drug Discovery</h2>
+
+[![Open In AI Workbench](https://img.shields.io/badge/Open_In-AI_Workbench-76B900)](https://ngc.nvidia.com/open-ai-workbench/aHR0cHM6Ly9naXRodWIuY29tL05WSURJQS1CaW9OZU1vLWJsdWVwcmludHMvZ2VuZXJhdGl2ZS12aXJ0dWFsLXNjcmVlbmluZw==)
 
 ![Generative Virtual Screening for Drug Discovery-r2 (1)](https://github.com/user-attachments/assets/dbb1795a-7e3f-4363-9b20-4bc4d67d04bb)
 
@@ -15,6 +17,36 @@ cd ../src
 jupyter notebook
 ```
 
+### Quick Start With NVIDIA AI Workbench
+
+[NVIDIA AI Workbench](https://www.nvidia.com/en-us/deep-learning-ai/solutions/data-science/workbench/) is a development environment manager that enables data scientists and developers to create, customize, collaborate, and migrate AI workloads and applications seamlessly across GPU systems, from laptops to cloud.
+
+If you do not have NVIDIA AI Workbench installed, first complete the installation for AI Workbench [here](https://docs.nvidia.com/ai-workbench/user-guide/latest/installation/overview.html). 
+
+1. Fork this Project to your own GitHub namespace and copy the link
+
+   ```
+   https://github.com/[your_namespace]/<project_name>
+   ```
+
+2. Open the NVIDIA AI Workbench App. Select a location to work in. 
+
+3. Clone this Project onto your desired machine by selecting **Clone Project** and providing the GitHub link.
+
+4. Wait for the project to build. You can expand the bottom **Building** indicator to view real-time build logs. 
+
+5. When the build completes, set the following configurations.
+
+   * `Environment` &rarr; `Secrets`. Specify the ``NGC_CLI_API_KEY`` as a project secret. [Details](https://github.com/NVIDIA-NIM-Agent-Blueprints/generative-virtual-screening/tree/main/deploy#get-your-api-key)
+
+6. Under `Environment` &rarr; `Compose`, select **Start**. This will pull and start the three NIM containers in the blueprint, which can take some time. 
+
+   * You can track progress under the ``Output`` widget. Specify ``Compose`` from the dropdown menu. 
+
+7. On the top right of the AI Workbench window, select **Open Jupyterlab**. It should automatically open in a new browser window.
+
+8. Navigate to ``src/generative-virtual-screening.ipynb`` and get started!
+
 ### Get Started
 
 * [Deploy](deploy/)

deploy/README.md

@@ -4,6 +4,15 @@ In this directory you will find a Docker Compose yaml file that will start up
 the NIMs for AlphaFold-2, MolMIM, and DiffDock. Once the NIMs are started, you
 will probably then want to take a look the example notebook in the `src` directory.
 
+#### NVIDIA AI Workbench
+
+For developers using [NVIDIA AI Workbench](https://www.nvidia.com/en-us/deep-learning-ai/solutions/data-science/workbench/) to run this blueprint, 
+
+1. Deploy the NIMs by selecting **Start** under ``Environment`` > ``Compose``.
+2. Select **Open Jupyterlab** on the top right and get started with the [notebook](../src/generative-virtual-screening.ipynb).
+
+Full quickstart instructions can be found [here](https://github.com/NVIDIA-AI-Blueprints/generative-virtual-screening/blob/main/README.md#quick-start-with-nvidia-ai-workbench). For all other users, continue below.
+
 ### Prerequisites
 
 You will need to install:

deploy/docker-compose.yaml

@@ -2,33 +2,42 @@ version: '3'
 
 services:
   alphafold:
-    image: nvcr.io/nim/deepmind/alphafold2:1.0.0
+    image: nvcr.io/nim/deepmind/alphafold2:2.1.0
     runtime: nvidia
     ports:
       - "8081:8000"
     volumes:
-      - ${ALPHAFOLD2_CACHE:-~/.cache/nim/models}:/opt/nim/.cache/
+      - type: bind
+        source: ${ALPHAFOLD2_CACHE:-~/.cache/nim}
+        target: /opt/nim/.cache/
     environment:
-      - NGC_CLI_API_KEY=${NGC_CLI_API_KEY:?Error NGC_CLI_API_KEY not set}
+      - NGC_API_KEY=${NGC_CLI_API_KEY:?Error NGC_CLI_API_KEY not set}
+      - NVWB_TRIM_PREFIX=true
 
   diffdock:
     image: nvcr.io/nim/mit/diffdock:1.2.0
     runtime: nvidia
     ports:
       - "8082:8000"
     volumes:
-      - ${DIFFDOCK_CACHE:-~/.cache/nim}:/home/nvs/.cache/nim/models/
+      - type: bind
+        source: ${DIFFDOCK_CACHE:-~/.cache/nim}
+        target: /opt/nim/.cache/nim/models/
 
     environment:
       - NGC_CLI_API_KEY=${NGC_CLI_API_KEY:?Error NGC_CLI_API_KEY not set}
       - NVIDIA_VISIBLE_DEVICES=${DIFFDOCK_VISIBLE_DEVICES:-0}
+      - NVWB_TRIM_PREFIX=true
 
   molmim:
     image: nvcr.io/nim/nvidia/molmim:1.0.0
     runtime: nvidia
     ports:
       - "8083:8000"
     volumes:
-      - ${MOLMIM_CACHE:-~/.cache/nim}:/home/nvs/.cache/nim/models/
+      - type: bind
+        source: ${MOLMIM_CACHE:-~/.cache/nim}
+        target: /opt/nim/.cache/nim/models/
     environment:
       - NGC_CLI_API_KEY=${NGC_CLI_API_KEY:?Error NGC_CLI_API_KEY not set}
+      - NVWB_TRIM_PREFIX=true

requirements.txt

@@ -0,0 +1,2 @@
+jupyterlab>3.0
+requests==2.31.0

src/README.md

@@ -12,4 +12,11 @@ instance and get going immediately!
 jupyter notebook
 ```
 
+#### NVIDIA AI Workbench
 
+For developers using [NVIDIA AI Workbench](https://www.nvidia.com/en-us/deep-learning-ai/solutions/data-science/workbench/) to run this blueprint, 
+
+1. Deploy the NIMs by selecting **Start** under ``Environment`` > ``Compose``.
+2. Select **Open Jupyterlab** on the top right and get started with the [notebook](./generative-virtual-screening.ipynb).
+
+Full quickstart instructions can be found [here](https://github.com/NVIDIA-AI-Blueprints/generative-virtual-screening/blob/main/README.md#quick-start-with-nvidia-ai-workbench).