# Resource configuration

This page covers the configuration of resources for single-node transforms. Distributed Spark transforms are configured with [Spark profiles](../../optimizing-pipelines/apply-spark-profiles/index.md) so that controls can be placed on large resource consumption.

Transform resources can be configured by calling `with_resources` on the transform decorator.

```python
from transforms.api import transform, Input, Output


@transform.using(
    output=Output("/path/output"),
    input=Input("/path/input"),
).with_resources(
    cpu_cores=1
)
def my_compute_function(output, input):
    ...
```

The following options can be configured:

* **`cpu_cores`:** The number of CPU cores to request for the transform's container. Can be a fraction, defaults to two.
* **`memory_gb`:** The amount of memory to request for the container, in GB.
* **`memory_mb`:** The amount of memory to request for the container, in MB.
* **`gpu_type`:** The type of GPU to allocate for the transform, provided as a string.

:::callout{theme="warning"}
Only one of `memory_gb` and `memory_mb` can be configured.
:::

By default, the maximum allowed values are 8 cores and 64 GBs of memory. To increase these limits, contact Palantir Support.

Below is an example configuration of cores, memory, and GPU type.

```python
from transforms.api import transform, Input, Output


@transform.using(
    output=Output("/path/output"),
    input=Input("/path/input"),
).with_resources(
    cpu_cores=8,
    memory_gb=32,
    gpu_type="NVIDIA_T4"
)
def my_compute_function(output, input):
    ...
```

Use the [Metrics telemetry tab](../metrics/index.md) to monitor build memory and CPU utilization and tune resource settings. Metrics are available for transforms that use lightweight compute engines.

## GPU provisioning

GPUs can only be used in Python transforms if they are [available to the project](../../resource-management/resource-queues/index.md#use-gpus) that the transform is in. Not all GPU types are available on all environments.

The following are GPU types that can be made available for transforms:

* `"NVIDIA_T4"`
* `"NVIDIA_V100"`
* `"NVIDIA_A10G"`
* `"NVIDIA_A100"`
* `"NVIDIA_A16"`
* `"NVIDIA_H100"`
* `"NVIDIA_H200"`
* `"NVIDIA_L4"`
* `"NVIDIA_L40S"`

For examples of GPU usage, including GPU-accelerated Polars with cuDF, see [Advanced single-node compute options](../advanced-compute/index.md).