Installing and Upgrading Driverless AI¶
Driverless AI can run on machines with only CPUs or machines with CPUs and GPUs. For the best (and intended-as-designed) experience, install Driverless AI on modern data center hardware with GPUs and CUDA support. Feature engineering and model building are primarily performed on CPU and GPU respectively. For this reason, Driverless AI benefits from multi-core CPUs with sufficient system memory and GPUs with sufficient RAM. For best results, we recommend GPUs that use the Pascal or Volta architectures. (Note that the older K80 and M60 GPUs available in EC2 are supported, but are not as fast.) In particular, image and NLP use cases significantly benefit from GPU usage.
Driverless AI supports local, LDAP, and PAM authentication. Authentication can be configured by setting environment variables or via a config.toml file. Refer to the Configuring Authentication section for more information. Note that the default authentication method is “unvalidated.”
Driverless AI also supports HDFS, S3, Google Cloud Storage, Google Big Query, KDB, Minio, and Snowflake access. Support for these data sources can be configured by setting environment variables for the data connectors or via a config.toml file. Refer to the Enabling Data Connectors section for more information.
- Linux X86_64 Installs
- IBM Power Installs
- Mac OS X
- Windows 10