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https://github.com/catalyst-team/alchemy
Experiments logging & visualization
https://github.com/catalyst-team/alchemy
deep-learning experiment-track infrastructure keras machine-learning pytorch reinforcement-learning reproducibility research tensorflow
Last synced: about 2 months ago
JSON representation
Experiments logging & visualization
- Host: GitHub
- URL: https://github.com/catalyst-team/alchemy
- Owner: catalyst-team
- License: apache-2.0
- Created: 2019-12-01T18:44:36.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-09-13T05:57:38.000Z (about 3 years ago)
- Last Synced: 2024-05-21T16:33:48.760Z (4 months ago)
- Topics: deep-learning, experiment-track, infrastructure, keras, machine-learning, pytorch, reinforcement-learning, reproducibility, research, tensorflow
- Language: Python
- Homepage: https://alchemy.host
- Size: 50.8 KB
- Stars: 50
- Watchers: 7
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome-deep-learning-tools - Alchemy - Experiments logging & visualization (2.For Experiment / Experiments management)
README
**Experiments logging & visualization**
[](https://www.codefactor.io/repository/github/catalyst-team/alchemy)
[](https://pypi.org/project/alchemy/)
[](https://catalyst-team.github.io/catalyst/index.html)
[](https://pepy.tech/project/alchemy)
[](https://twitter.com/CatalystTeam)
[](https://t.me/catalyst_team)
[](https://join.slack.com/t/catalyst-team-devs/shared_invite/zt-d9miirnn-z86oKDzFMKlMG4fgFdZafw)
[](https://github.com/catalyst-team/alchemy/graphs/contributors)
Project [manifest](https://github.com/catalyst-team/catalyst/blob/master/MANIFEST.md). Part of [Catalyst Ecosystem](https://docs.google.com/presentation/d/1D-yhVOg6OXzjo9K_-IS5vSHLPIUxp1PEkFGnpRcNCNU/edit?usp=sharing):
- [Alchemy](https://github.com/catalyst-team/alchemy) - Experiments logging & visualization
- [Catalyst](https://github.com/catalyst-team/catalyst) - Accelerated Deep Learning Research and Development
- [Reaction](https://github.com/catalyst-team/reaction) - Convenient Deep Learning models serving
---
## Installation
Common installation:
```bash
pip install -U alchemy
```
Previous name `alchemy-catalyst` [](https://pepy.tech/project/alchemy-catalyst)
## Getting started
1. Goto [Alchemy](https://alchemy.host/) and get your personal token.
2. Run following **example.py**:
```python
import random
from alchemy import Logger
# insert your personal token here
token = "..."
project = "default"
for gid in range(1):
group = f"group_{gid}"
for eid in range(2):
experiment = f"experiment_{eid}"
logger = Logger(
token=token,
experiment=experiment,
group=group,
project=project,
)
for mid in range(4):
metric = f"metric_{mid}"
# let's sample some random data
n = 300
x = random.randint(-10, 10)
for i in range(n):
logger.log_scalar(metric, x)
x += random.randint(-1, 1)
logger.close()
```
3. Now you should see your metrics on [Alchemy](https://alchemy.host/).
## Catalyst.Ecosystem
1. Goto [Alchemy](https://alchemy.host/) and get your personal token.
2. Log your Catalyst experiment with **AlchemyLogger**:
```python
from catalyst.dl import SupervisedRunner, AlchemyLogger
runner = SupervisedRunner()
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
loaders=loaders,
logdir=logdir,
num_epochs=num_epochs,
verbose=True,
callbacks={
"logger": AlchemyLogger(
token="...", # your Alchemy token
project="your_project_name",
experiment="your_experiment_name",
group="your_experiment_group_name",
)
}
)
```
3. Now you should see your metrics on [Alchemy](https://alchemy.host/).
## Examples
For mode detailed tutorials, please follow [Catalyst examples](https://github.com/catalyst-team/catalyst/tree/master/examples#tutorials).