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Frequently Asked Questions


How do I install the CLI?

pip install determined-cli

For more details, see the installation instructions.

When trying to install the Determined command-line interface, I encounter this distutils error

Uninstalling a distutils installed project (...) has been deprecated and will be removed in a future version. This is due to the fact that uninstalling a distutils project will only partially uninstall the project.

If a Python library has previously been installed in your environment with distutils or conda, pip may not be able to upgrade or downgrade the library to the version required by Determined. There are two recommended solutions:

  1. Install the Determined command-line interface into a fresh virtualenv with no previous Python packages installed.

  2. Use --ignore-installed with pip to force overwriting the library version(s).

    pip install --ignore-installed determined-cli

Packages and Containers

How do I install Python packages that my model code depends on?

By default, workloads execute inside a Determined-provided container that includes common deep learning libraries and frameworks. If your model code has additional dependencies, the easiest way to install them is to specify a container startup hook. For more complex dependencies, you can also use a custom Docker image.

Can I use a custom container image?

Yes; see the documentation on custom Docker images for details.

Can I use Determined with a private Docker Registry?

Yes: specify the registry path as part of the custom image name. See the documentation on custom Docker images for more details.

Multi-GPU Training

Why do my multi-GPU training experiments never start?

It might be that slots_per_trial in the experiment configuration is not a multiple of the number of GPUs on a machine or that there are running tasks preventing your multi-GPU trials from acquiring all the GPUs on a single machine. Consider adjusting slots_per_trial or terminating existing tasks to free up slots in your cluster.

See Distributed Training for more details.

Why do my multi-machine training experiments appear to be stuck?

Multi-machine training requires that all machines be able to connect to each other directly. There may be firewall rules or network configuration that prevent machines in your cluster from communicating. Please check if agent machines can access each other outside of Determined (e.g., using the ping or netcat tools).

More rarely, if agents have multiple network interfaces and some of them are not routable, Determined may pick one of those interfaces rather than one that allows one agent to contact another. In this case, it is possible to set the network interface used for multi-GPU training explicitly in the Cluster Configuration.

TensorFlow Support

Can I train a Tensorflow Core model in Determined?

Determined has support for TensorFlow models that use the tf.keras or Estimator APIs. For models that use the low-level TensorFlow Core APIs, we recommend porting your model to use Estimator Trial. Example of converting a Tensorflow graph into an Estimator.