https://github.com/foundation-model-stack/fm-training-estimator
Estimate resources needed to train LLMs
https://github.com/foundation-model-stack/fm-training-estimator
Last synced: 4 months ago
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Estimate resources needed to train LLMs
- Host: GitHub
- URL: https://github.com/foundation-model-stack/fm-training-estimator
- Owner: foundation-model-stack
- License: apache-2.0
- Created: 2024-07-31T08:33:14.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2026-02-10T10:22:36.000Z (5 months ago)
- Last Synced: 2026-03-03T00:35:46.769Z (4 months ago)
- Language: Python
- Homepage:
- Size: 1.13 MB
- Stars: 14
- Watchers: 9
- Forks: 9
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: code-of-conduct.md
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README
# FM Training Estimator
Estimators for Large Language Model Training.
Estimate resource consumption - memory, tokens, time etc for training and fine-tuning jobs using an hybrid of theory and learned regression models.
## Feature Matrix and Roadmap
| Technique | Support |
|--------------------|--------------------|
| Full (1 gpu) | :heavy_check_mark: |
| FSDP (multi) | :heavy_check_mark: |
| Lora (1 gpu) | :heavy_check_mark: |
| QLora (1 gpu) | :heavy_check_mark: |
| Speculators | Planned |
| Tensor Parallelism | Planned |
### Time
Full learned approach. Coverage based on availability of training data.
### Memory
Hybrid theory + learned. Coverage of learned approach is subject to availability of training data.
### Tokens
Fully theory. Simulation based models available.
| Technique | Explanation | Availability |
|-----------|------------------------------------------------|--------------------|
| TE0 | Simulation based - slow but accurate | :heavy_check_mark: |
| TE1 | Statistical | Planned |
| TE2 | Approximate - fast, light, reasonable accurate | :heavy_check_mark |
## Usage
You can use the library `fm_training_estimator` as a Python package by installing it via pip, see [installation](#install), [build a regression model](#build-a-regression-model-for-learned-prediction-method) and [using the library](#use-the-library-to-get-estimates). If you'd like to construct the estimator service with a [Web UI](#make-estimates-via-a-web-ui) via FastAPI or [build a docker image](#build-a-docker-container-image), clone the repository in your local machine before following the instructions in those sections.
Within your working directory, it is recommended to create a virtual environment to ensure no conflicts in dependencies.
```
python -m venv .venv
source .venv/bin/activate
```
### Install
```
pip install fm_training_estimator
```
### Build a regression model for learned prediction method
Now, prepare data in the expected format for lookup and regression. The format to be used to save this data is given [here](https://github.com/foundation-model-stack/fm-training-estimator/tree/main/fm_training_estimator/data/README.md). Save your data file into `./workdir/data.csv`.
```
mkdir workdir
mv ./workdir/data.csv
```
Now, build a regression model using this data, using one of the the provided make targets.
This will create a model called `./workdir/model.zip` which you can then use to estimate the resource consumption.
You can now run the estimator library, see below.
### Using the Estimator
There are a few ways to use the Estimator now:
1. Using the CLI tool, passing in a config in json format.
2. Using the Web UI.
3. Using the SDK directly from Python code.
#### Using the CLI
### Make estimates via a Web UI
To do this, first prepare a txt file called `model_whitelist.txt` in the `workdir/` with a list of model names, 1 per line. Note that these are the models on which you want to run the estimator to estimate their resource consumption. You can use the provided [example](https://github.com/foundation-model-stack/fm-training-estimator/blob/main/fm_training_estimator/ui/model_whitelist.txt) and place it in your `workdir`. Modify this list as needed.
Now, run the ui:
```
make run-web-ui
```
This will start the UI on `localhost:3000` port.
(The web ui has other options, not covered in this simple setup. If you want to skip the model whitelisting or change the port, directly run the UI as shown in the README in the `./fm_training_estimator/ui` folder.)
#### Use the library to get estimates
For a full API reference, visit our [readthedocs](link).
Example code:
```python
# Standard
import os
# First Party
from fm_training_estimator.config.arguments import (
DataArguments,
EstimateInput,
EstimatorMetadata,
FMArguments,
HFTrainingArguments,
InfraArguments,
JobConfig,
)
from fm_training_estimator.sdk import (
estimate_cost,
estimate_memory,
estimate_time,
estimate_tokens,
)
workdir_path = os.path.join(os.path.abspath(os.curdir), "workdir")
model_path = os.path.join(workdir_path, "model.json")
lookup_data_path = os.path.join(workdir_path, "data.csv")
estimator_metadata = EstimatorMetadata(base_data_path=lookup_data_path)
fm = FMArguments(
base_model_path="ibm-granite/granite-7b-base",
torch_dtype="bfloat16",
block_size=1024,
)
hf_training = HFTrainingArguments(
per_device_train_batch_size=1, gradient_checkpointing=False
)
data = DataArguments(dataset="imdb", te_approach=0)
infra = InfraArguments(numGpusPerPod=1)
job_conf = JobConfig(hf_training, fm, data, infra)
est_input = EstimateInput(estimator_metadata=estimator_metadata, job_configs=[job_conf])
print("Estimating Memory:....")
print("With only theory: ", estimate_memory(est_input))
print("With reg model: ", estimate_memory(est_input, model_path))
hf_training.fsdp = "full_shard"
print("Using fsdp full shard")
print("With only theory: ", estimate_memory(est_input))
print("With reg model: ", estimate_memory(est_input, model_path))
print("Estimating Time:....")
print("With only theory: ", estimate_time(est_input))
print("With reg model: ", estimate_time(est_input, model_path))
print("Estimating Tokens:....")
print("With only theory: ", estimate_tokens(est_input))
print("With reg model: ", estimate_tokens(est_input, model_path))
```
### Build a Docker Container Image
To build the estimator container image:
1. Make sure both `model.json` and `data.csv` files are present in the `workdir` folder.
2. Use this command to build and push the image:
```shell
make cbuild
make cpush # If you want to push to the container registry
```
3. Use this command to run the image:
```shell
docker run --rm -it -v "/path/to/input.json:/app/input.json" icr.io/ftplatform/fm_training_estimator:latest
```