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Why does my code work locally but fail with Grid Run?

This is likely an environment mismatch. For Python users we recommend testing your code in a virtual environment with Python or Conda and then running a pip freeze to get all the necessary packages. For example:

# Create conda environment
conda create --name test-environment
pip install -r requirments.txt

# ensure code works

# copy dependencies to requirements.txt
pip freeze > requirements.txt

How do I download my run/experiment artifacts?

From the CLI

It's as easy as running grid artifacts my-run-name! This will download all artifacts fromthe run into a new directory called grid_artifacts.

From the UI

How long are artifacts stored?

Artifacts are stored until the run or experiment that generated the artifacts is deleted.

Why isn't Grid locating my requirements.txt file?

Grid by default will look for a requirements.txt file in the root directory of your project. To customize this behavior try using the --dependency_file flag. An example is below: grid run --dependency_file ./path/to/requirements.txt

How can I resolve Windows pathing issues when using Grid?

There are two options users can try:

  1. default to the Windows Subsystem for Linux
  2. Try running your commands with by replacing \ characters with \\ in your path.

The cost changed during a run?

Grid provides estimates of ongoing costs. Once a run terminates we compute the final cost.

How do I find out what packages are pre-installed in the cloud machine?

Cloud machines are configured in a simple way with only what is needed to run the scripts in the framework of choice.

What machine learning frameworks does Grid support?

Grid is optimized for PyTorch Lightning. It also supports Tensorflow, Keras, or any framework built on top of PyTorch.

Try this repository for running Keras example:

Grid can also run non-deep learning focused workloads such as plain numpy, sklearn, etc..

I'm using an in-house ML library. Can I use it with Grid?

Grid can run arbitrary python scripts. You're free to run whatever you want inside a script. However, Grid is optimized for Pytorch, Pytorch Lightning, Tensorflow, Keras, numpy and sklearn.

I am getting lot of errors using CLI

Grid supports Linux based operating systems. We recommend using virtual environment when using CLI. Please see the guidance here

Experiments are queued for a long time

If experiments are queued for a long time it could be a sign that instance type requested is not available; reach out on slack or open a github issue if you see this: Choosing another instance type may help in some cases.

If the following FAQ didn't help resolve your issue please file a support ticket at or reach out to the community at community Slack.