I need this to run CPU only? def __init__(self, model_name, gpu_id, max_memory):
device = torch.device(‘cuda’, gpu_id) # TODO: allow sending to cpu
# recommended default for devices with > 40 GB VRAM
# load model onto one device
if max_memory is None:
self._model = AutoModelForCausalLM.from_pretrained(
model_name, torch_dtype=torch.float16, device_map=”auto”)
self._model.to(device)
To modify the code to run on CPU only, you need to make a few changes. Here’s an updated version of the code that runs on CPU:
“`python
import torch
def __init__(self, model_name):
device = torch.device(‘cpu’)
self._model = AutoModelForCausalLM.from_pretrained(model_name)
self._model.to(device)
“`
In this version, the `gpu_id` and `max_memory` parameters have been removed since they are not necessary for CPU execution. The device is set to `’cpu’`, and the model is loaded onto the CPU using the `to(device)` method.
Note that the `torch.float16` data type argument has been omitted because it is not commonly used on CPUs. If you specifically need to use `torch.float16` on CPU, you may need to check if your hardware and software support it.
can this point the RAM? # create a device_map from max_memory
device_map = infer_auto_device_map(
model_from_conf,
max_memory=max_memory,
no_split_module_classes=[“GPTNeoXLayer”],
dtype=”float16″
The code snippet you provided is incomplete, but based on what you’ve shared, it seems that the `infer_auto_device_map()` function is used to create a device map based on the `max_memory` parameter. However, the provided code doesn’t specify how the device map is used or what it does exactly.
To answer your question, pointing the RAM explicitly is not necessary in PyTorch. When you load a model onto a device (CPU or GPU), PyTorch automatically manages the memory allocation. The memory used by the model and its associated tensors will be allocated in the available RAM based on the device you specify.
If you want to limit the memory usage of your model, you can set the `max_memory` parameter to a value that suits your needs. This parameter is typically used to control the memory allocation on GPU devices, not specifically for pointing to RAM. However, when running on CPU, memory usage is typically managed by the operating system, so explicitly limiting it may not be necessary.
If you have specific memory requirements or constraints, please provide more context or details so that I can assist you further.
conflicts for:
pyarrow=8.0.0 -> numpy[version=’>=1.16, pypy3.8[version=’7.3.11.*|>=7.3.11|>=7.3.9|>=7.3.8|7.3.9.*|7.3.8.*’]
python-snappy=0.6.1 -> python[version=’>=3.8, pypy3.8[version=’7.3.11.*|7.3.9.*|7.3.8.*’]
fastparquet=0.6.3 -> numpy -> pypy3.8[version=’7.3.11.*|>=7.3.11|>=7.3.9|>=7.3.8|7.3.9.*|7.3.8.*’]
pip -> python[version=’>=3.7′] -> pypy3.8[version=’7.3.11.*|7.3.9.*|7.3.8.*|>=7.3.9|>=7.3.8′]
faiss-cpu -> numpy[version=’>=1.16, pypy3.8[version=’7.3.11.*|>=7.3.11|>=7.3.9|>=7.3.8|7.3.9.*|7.3.8.*’]
The conflict arises because some of the packages you mentioned (`pyarrow`, `python-snappy`, `fastparquet`, `pip`, and `faiss-cpu`) have dependencies on `numpy`, and `numpy` has a dependency on `pypy3.8` with specific version constraints.
To resolve this conflict, you can try the following steps:
1. Check if you have a specific requirement for `pypy3.8` or if you can use a different Python interpreter, such as CPython. If you can use CPython, it may help resolve the conflicts.
2. Update the conflicting packages to their latest versions, if possible. You can use the following command to update the packages:
“`
pip install –upgrade pyarrow python-snappy fastparquet pip faiss-cpu
“`
This command will attempt to update the packages to their latest versions while resolving the dependency conflicts. If the conflicts are caused by outdated package versions, updating them might resolve the issue.
3. If updating the packages doesn’t resolve the conflict, you may need to manually specify compatible versions for the conflicting packages in your `requirements.txt` or `environment.yml` file. For example, you can try specifying a compatible version of `numpy` that satisfies the constraints of all the packages. Here’s an example of how you can specify a version range:
“`
numpy>=1.16,<2.0
“`
Adjust the version range as needed based on the constraints of the conflicting packages.
4. If none of the above steps work, you may need to reconsider your package requirements and check if there are alternative packages or versions that can be used without conflicting dependencies.
Remember to create a clean virtual environment or Conda environment to isolate your project and avoid conflicts with other packages installed globally on your system.