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H2O Driverless AI

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Description

H2O Driverless AI is an artificial intelligence (AI) platform that automates some of the most difficult data science and machine learning workflows such as feature engineering, model validation, model tuning, model selection and model deployment. It aims to achieve highest predictive accuracy, comparable to expert data scientists, but in much shorter time thanks to end-to-end automation. Driverless AI also offers automatic visualizations and machine learning interpretability (MLI).

Publisher

H2O.ai

Latest Tag

latest

Modified

September 24, 2020

Compressed Size

2 GB

Multinode Support

No

Multi-Arch Support

No

What is Driverless AI?

H2O Driverless AI is an artificial intelligence (AI) platform that automates some of the most difficult data science and machine learning workflows such as feature engineering, model validation, model tuning, model selection and model deployment. It aims to achieve highest predictive accuracy, comparable to expert data scientists, but in much shorter time thanks to end-to-end automation. Driverless AI also offers automatic visualizations and machine learning interpretability (MLI). Especially in regulated industries, model transparency and explanation are just as important as predictive performance.

Driverless AI enables users of all backgrounds to draw the most value from their data. It was also specifically designed to take advantage of graphical processing units (GPUs), including multi-GPU workstations and servers such as the NVIDIA DGX-1 for order-of-magnitude faster training.

High-level capabilities:

  • Client/server application for rapid experimentation and deployment of state-of-the-art supervised machine learning models

  • Automatically creates machine learning modeling pipelines for highest predictive accuracy

  • Automatically creates stand-alone scoring pipeline for in-process scoring or client/server scoring via http or tcp protocols.

  • Python API or GUI

  • Multi-GPU and multi-CPU support for powerful workstations and NVidia DGX supercomputers

  • Machine Learning model interpretation module with global and local model interpretation Automatic Visualization module

Problem types supported:

  • Regression (continuous target variable, for age, income, house price, loss prediction)

  • Binary classification (0/1 or “N”/”Y”, for fraud prediction, churn prediction, failure prediction, etc.)

  • Multinomial classification (0/1/2/3 or “A”/”B”/”C”/”D” for categorical target variables, for prediction of membership type, next-action, product recommendation, etc.)

Data types supported:

  • Tabular structured data, rows are observations, columns are fields/features/variables i.i.d. (identically and independently distributed) data

  • Numeric, categorical and textual fields

  • Missing values are allowed

  • Time-series data with a single time-series (time flows across the entire dataset, not per block of data)

Data types NOT supported:

  • Image/video/audio

  • Grouped time-series (e.g., sales per item per store per hour, all in one file)

File formats supported:

  • Plain text formats of columnar data (.csv, .tsv, .txt)

  • Compressed archives (.zip, .gz)

Licensing

Driverless AI is a commercial product. Please contact sales@h2o.ai for more information about an enterprise license.

To obtain a trial license key, please visit https://www.h2o.ai/try-driverless-ai/ and signup to get your 21-day trial key by email.

Running Driverless AI

Notes: These installation steps assume that you have a license key for Driverless AI. Either use a trial key from the instructions above, or, for information on how to purchase a commercial license key for Driverless AI, contact sales@h2o.ai. Driverless AI is only supported using NVIDIA Docker. NVIDIA Docker is pre-installed on NVIDIA DGX systems and can be manually installed on other systems. Open a Terminal window, and ssh to the machine that will run Driverless AI. Once you are logged in, perform the following steps.

Step 1. Pull the Driverless AI docker image: In the Tags section, locate the container image release that you want to run. In the Actions column, hover over the white space to display the Docker command icon. Click the icon to display the docker pull command. Copy the docker pull command and click Close. Open a command prompt and paste the pull command. The pulling of the container image begins. Ensure the pull completes successfully before proceeding to the next step.

Step 2. Set up the data and log directories on the host machine:

Set up the data and log directories on the host machine
mkdir data
mkdir log
mkdir tmp
mkdir license

Step 3. At this point, you can copy data into the data directory on the host machine. The data will be visible inside the docker container.

Step 4. Start the Driverless AI docker image:

#Start the Driverless AI docker image
nvidia-docker run \
    --rm \
    -u `id -u`:`id -g` \
    -p 12345:12345 \
    -p 54321:54321 \
    -p 8888:8888 \
    -v `pwd`/data:/data \
    -v `pwd`/log:/log \
    -v `pwd`/license:/license \
    -v `pwd`/tmp:/tmp \
    nvcr.io/nvidia_partners/h2oai-driverless:latest

Driverless AI will begin running:

---------------------------------
Welcome to H2O.ai's Driverless AI
---------------------------------

- Put data in the volume mounted at /data
- Logs are written to the volume mounted at /log/YYYYMMDD-HHMMSS
- Connect to Driverless AI on port 12345 inside the container
- Connect to Jupyter notebook on port 8888 inside the container

Step 5. (Optional) To view the logs, open another Terminal window and ssh to the host machine running Driverless AI. Once you are logged in, run the following:

tail -f log/YYYYMMDD-HHMMSS/h2oai.log

Step 6. Connect to Driverless AI with the Chrome browser:

http://Your-Driverless-AI-Host-Machine:12345

Documentation and Suggested Webinars

User Documentation
Webinars

DriverlessAI Support

You can email us at support@h2o.ai, or you can try posting your question to the community.