aim

Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

Science Score: 67.0%

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Found 1 DOI reference(s) in README
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3 of 82 committers (3.7%) from academic institutions
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Low similarity (8.9%) to scientific vocabulary

Keywords

ai data-science data-visualization experiment-tracking machine-learning metadata metadata-tracking ml mlflow mlops prompt-engineering python pytorch tensorboard tensorflow visualization

Keywords from Contributors

mesh distribution exoplanet standardization interactive confidence-intervals agents application

Last synced: 4 months ago · JSON representation ·

Repository

Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

Basic Info

Host: GitHub
Owner: aimhubio
License: apache-2.0
Language: Python
Default Branch: main
Homepage: https://aimstack.io
Size: 64.4 MB

Statistics

Stars: 5,776
Watchers: 41
Forks: 351
Open Issues: 433
Releases: 0

Topics

ai data-science data-visualization experiment-tracking machine-learning metadata metadata-tracking ml mlflow mlops prompt-engineering python pytorch tensorboard tensorflow visualization

Created over 6 years ago · Last pushed 4 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation Codeowners

README.md

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An easy-to-use & supercharged open-source experiment tracker

Aim logs your training runs and any AI Metadata, enables a beautiful UI to compare, observe them and an API to query them programmatically.

[![Discord Server](https://dcbadge.vercel.app/api/server/zXq2NfVdtF?compact=true&style=flat)](https://community.aimstack.io/) [![Twitter Follow](https://img.shields.io/twitter/follow/aimstackio?style=social)](https://twitter.com/aimstackio) [![Medium](https://img.shields.io/badge/Medium-12100E?style=flat&logo=medium&logoColor=white)](https://medium.com/aimstack) [![Platform Support](https://img.shields.io/badge/platform-Linux%20%7C%20macOS-blue)]() [![PyPI - Python Version](https://img.shields.io/badge/python-%3E%3D%203.7-blue)](https://pypi.org/project/aim/) [![PyPI Package](https://img.shields.io/pypi/v/aim?color=yellow)](https://pypi.org/project/aim/) [![License](https://img.shields.io/badge/License-Apache%202.0-orange.svg)](https://opensource.org/licenses/Apache-2.0) [![PyPI Downloads](https://img.shields.io/pypi/dw/aim?color=green)](https://pypi.org/project/aim/) [![Issues](https://img.shields.io/github/issues/aimhubio/aim)](http://github.com/aimhubio/aim/issues)

_{SEAMLESSLY INTEGRATES WITH:}

_{TRUSTED BY ML TEAMS FROM:}

AimStack offers enterprise support that's beyond core Aim. Contact via hello@aimstack.io e-mail.

About • Demos • Ecosystem • Quick Start • Examples • Documentation • Community • Blog

ℹ️ About

Aim is an open-source, self-hosted ML experiment tracking tool designed to handle 10,000s of training runs.

Aim provides a performant and beautiful UI for exploring and comparing training runs. Additionally, its SDK enables programmatic access to tracked metadata — perfect for automations and Jupyter Notebook analysis.

Aim's mission is to democratize AI dev tools 🎯

Log Metadata Across Your ML Pipeline 💾	Visualize & Compare Metadata via UI 📊
ML experiments and any metadata tracking Integration with popular ML frameworks Easy migration from other experiment trackers	Metadata visualization via Aim Explorers Grouping and aggregation Querying using Python expressions
Run ML Trainings Effectively ⚡	Organize Your Experiments 🗂️
System info and resource usage tracking Real-time alerting on training progress Logging and configurable notifications	Detailed run information for easy debugging Centralized dashboard for holistic view Runs grouping with tags and experiments

🎬 Demos

Check out live Aim demos NOW to see it in action.

🌍 Ecosystem

Aim is not just an experiment tracker. It's a groundwork for an ecosystem. Check out the two most famous Aim-based tools.

🏁 Quick start

Follow the steps below to get started with Aim.

1. Install Aim on your training environment

shell pip3 install aim

2. Integrate Aim with your code

```python from aim import Run

Initialize a new run

run = Run()

Log run parameters

run["hparams"] = { "learningrate": 0.001, "batchsize": 32, }

Log metrics

for i in range(10): run.track(i, name='loss', step=i, context={ "subset":"train" }) run.track(i, name='acc', step=i, context={ "subset":"train" }) ```

See the full list of supported trackable objects(e.g. images, text, etc) here.

3. Run the training as usual and start Aim UI

shell aim up

Learn more

Migrate from other tools

Aim has built-in converters to easily migrate logs from other tools. These migrations cover the most common usage scenarios. In case of custom and complex scenarios you can use Aim SDK to implement your own conversion script. - [TensorBoard logs converter](https://aimstack.readthedocs.io/en/latest/quick_start/convert_data.html#show-tensorboard-logs-in-aim) - [MLFlow logs converter](https://aimstack.readthedocs.io/en/latest/quick_start/convert_data.html#show-mlflow-logs-in-aim) - [Weights & Biases logs converter](https://aimstack.readthedocs.io/en/latest/quick_start/convert_data.html#show-weights-and-biases-logs-in-aim)

Integrate Aim into an existing project

Aim easily integrates with a wide range of ML frameworks, providing built-in callbacks for most of them. - [Integration with Pytorch Ignite](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-pytorch-ignite) - [Integration with Pytorch Lightning](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-pytorch-lightning) - [Integration with Hugging Face](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-hugging-face) - [Integration with Keras & tf.Keras](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-keras-tf-keras) - [Integration with Keras Tuner](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-keras-tuner) - [Integration with XGboost](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-xgboost) - [Integration with CatBoost](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-catboost) - [Integration with LightGBM](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-lightgbm) - [Integration with fastai](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-fastai) - [Integration with MXNet](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-mxnet) - [Integration with Optuna](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-optuna) - [Integration with PaddlePaddle](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-paddlepaddle) - [Integration with Stable-Baselines3](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-stable-baselines3) - [Integration with Acme](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-acme) - [Integration with Prophet](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-prophet)

Query runs programmatically via SDK

Aim Python SDK empowers you to query and access any piece of tracked metadata with ease. ```python from aim import Repo my_repo = Repo('/path/to/aim/repo') query = "metric.name == 'loss'" # Example query # Get collection of metrics for run_metrics_collection in my_repo.query_metrics(query).iter_runs(): for metric in run_metrics_collection: # Get run params params = metric.run[...] # Get metric values steps, metric_values = metric.values.sparse_numpy() ```

Set up a centralized tracking server

Aim remote tracking server allows running experiments in a multi-host environment and collect tracked data in a centralized location. See the docs on how to [set up the remote server](https://aimstack.readthedocs.io/en/latest/using/remote_tracking.html).

Deploy Aim on kubernetes

- The official Aim docker image: https://hub.docker.com/r/aimstack/aim - A guide on how to deploy Aim on kubernetes: https://aimstack.readthedocs.io/en/latest/using/k8s_deployment.html

Read the full documentation on aimstack.readthedocs.io 📖

🆚 Comparisons to familiar tools

TensorBoard vs Aim

**Training run comparison** Order of magnitude faster training run comparison with Aim - The tracked params are first class citizens at Aim. You can search, group, aggregate via params - deeply explore all the tracked data (metrics, params, images) on the UI. - With tensorboard the users are forced to record those parameters in the training run name to be able to search and compare. This causes a super-tedius comparison experience and usability issues on the UI when there are many experiments and params. **TensorBoard doesn't have features to group, aggregate the metrics** **Scalability** - Aim is built to handle 1000s of training runs - both on the backend and on the UI. - TensorBoard becomes really slow and hard to use when a few hundred training runs are queried / compared. **Beloved TB visualizations to be added on Aim** - Embedding projector. - Neural network visualization.

MLflow vs Aim

MLFlow is an end-to-end ML Lifecycle tool. Aim is focused on training tracking. The main differences of Aim and MLflow are around the UI scalability and run comparison features. Aim and MLflow are a perfect match - check out the [aimlflow](https://github.com/aimhubio/aimlflow) - the tool that enables Aim superpowers on Mlflow. **Run comparison** - Aim treats tracked parameters as first-class citizens. Users can query runs, metrics, images and filter using the params. - MLFlow does have a search by tracked config, but there are no grouping, aggregation, subplotting by hyparparams and other comparison features available. **UI Scalability** - Aim UI can handle several thousands of metrics at the same time smoothly with 1000s of steps. It may get shaky when you explore 1000s of metrics with 10000s of steps each. But we are constantly optimizing! - MLflow UI becomes slow to use when there are a few hundreds of runs.

Weights and Biases vs Aim

Hosted vs self-hosted - Weights and Biases is a hosted closed-source MLOps platform. - Aim is self-hosted, free and open-source experiment tracking tool.

🛣️ Roadmap

Detailed milestones

The Aim product roadmap :sparkle:

The Backlog contains the issues we are going to choose from and prioritize weekly
The issues are mainly prioritized by the highly-requested features

High-level roadmap

The high-level features we are going to work on the next few months:

In progress

[ ] Aim SDK low-level interface
[ ] Dashboards – customizable layouts with embedded explorers
[ ] Ergonomic UI kit
[ ] Text Explorer

Next-up

**Aim UI** - Runs management - Runs explorer – query and visualize runs data(images, audio, distributions, ...) in a central dashboard - Explorers - Distributions Explorer **SDK and Storage** - Scalability - Smooth UI and SDK experience with over 10.000 runs - Runs management - CLI commands - Reporting - runs summary and run details in a CLI compatible format - Manipulations – copy, move, delete runs, params and sequences - Cloud storage support – store runs blob(e.g. images) data on the cloud - Artifact storage – store files, model checkpoints, and beyond **Integrations** - ML Frameworks: - Shortlist: scikit-learn - Resource management tools - Shortlist: Kubeflow, Slurm - Workflow orchestration tools

Done

- [x] Live updates (Shipped: _Oct 18 2021_) - [x] Images tracking and visualization (Start: _Oct 18 2021_, Shipped: _Nov 19 2021_) - [x] Distributions tracking and visualization (Start: _Nov 10 2021_, Shipped: _Dec 3 2021_) - [x] Jupyter integration (Start: _Nov 18 2021_, Shipped: _Dec 3 2021_) - [x] Audio tracking and visualization (Start: _Dec 6 2021_, Shipped: _Dec 17 2021_) - [x] Transcripts tracking and visualization (Start: _Dec 6 2021_, Shipped: _Dec 17 2021_) - [x] Plotly integration (Start: _Dec 1 2021_, Shipped: _Dec 17 2021_) - [x] Colab integration (Start: _Nov 18 2021_, Shipped: _Dec 17 2021_) - [x] Centralized tracking server (Start: _Oct 18 2021_, Shipped: _Jan 22 2022_) - [x] Tensorboard adaptor - visualize TensorBoard logs with Aim (Start: _Dec 17 2021_, Shipped: _Feb 3 2022_) - [x] Track git info, env vars, CLI arguments, dependencies (Start: _Jan 17 2022_, Shipped: _Feb 3 2022_) - [x] MLFlow adaptor (visualize MLflow logs with Aim) (Start: _Feb 14 2022_, Shipped: _Feb 22 2022_) - [x] Activeloop Hub integration (Start: _Feb 14 2022_, Shipped: _Feb 22 2022_) - [x] PyTorch-Ignite integration (Start: _Feb 14 2022_, Shipped: _Feb 22 2022_) - [x] Run summary and overview info(system params, CLI args, git info, ...) (Start: _Feb 14 2022_, Shipped: _Mar 9 2022_) - [x] Add DVC related metadata into aim run (Start: _Mar 7 2022_, Shipped: _Mar 26 2022_) - [x] Ability to attach notes to Run from UI (Start: _Mar 7 2022_, Shipped: _Apr 29 2022_) - [x] Fairseq integration (Start: _Mar 27 2022_, Shipped: _Mar 29 2022_) - [x] LightGBM integration (Start: _Apr 14 2022_, Shipped: _May 17 2022_) - [x] CatBoost integration (Start: _Apr 20 2022_, Shipped: _May 17 2022_) - [x] Run execution details(display stdout/stderr logs) (Start: _Apr 25 2022_, Shipped: _May 17 2022_) - [x] Long sequences(up to 5M of steps) support (Start: _Apr 25 2022_, Shipped: _Jun 22 2022_) - [x] Figures Explorer (Start: _Mar 1 2022_, Shipped: _Aug 21 2022_) - [x] Notify on stuck runs (Start: _Jul 22 2022_, Shipped: _Aug 21 2022_) - [x] Integration with KerasTuner (Start: _Aug 10 2022_, Shipped: _Aug 21 2022_) - [x] Integration with WandB (Start: _Aug 15 2022_, Shipped: _Aug 21 2022_) - [x] Stable remote tracking server (Start: _Jun 15 2022_, Shipped: _Aug 21 2022_) - [x] Integration with fast.ai (Start: _Aug 22 2022_, Shipped: _Oct 6 2022_) - [x] Integration with MXNet (Start: _Sep 20 2022_, Shipped: _Oct 6 2022_) - [x] Project overview page (Start: _Sep 1 2022_, Shipped: _Oct 6 2022_) - [x] Remote tracking server scaling (Start: _Sep 11 2022_, Shipped: _Nov 26 2022_) - [x] Integration with PaddlePaddle (Start: _Oct 2 2022_, Shipped: _Nov 26 2022_) - [x] Integration with Optuna (Start: _Oct 2 2022_, Shipped: _Nov 26 2022_) - [x] Audios Explorer (Start: _Oct 30 2022_, Shipped: _Nov 26 2022_) - [x] Experiment page (Start: _Nov 9 2022_, Shipped: _Nov 26 2022_) - [x] HuggingFace datasets (Start: _Dec 29 2022_, _Feb 3 2023_)

👥 Community

Aim README badge

Add Aim badge to your README, if you've enjoyed using Aim in your work:

[![Aim](https://img.shields.io/badge/powered%20by-Aim-%231473E6)](https://github.com/aimhubio/aim)

Cite Aim in your papers

In case you've found Aim helpful in your research journey, we'd be thrilled if you could acknowledge Aim's contribution:

bibtex @software{Arakelyan_Aim_2020, author = {Arakelyan, Gor and Soghomonyan, Gevorg and {The Aim team}}, doi = {10.5281/zenodo.6536395}, license = {Apache-2.0}, month = {6}, title = {{Aim}}, url = {https://github.com/aimhubio/aim}, version = {3.9.3}, year = {2020} }

Contributing to Aim

Considering contibuting to Aim? To get started, please take a moment to read the CONTRIBUTING.md guide.

Join Aim contributors by submitting your first pull request. Happy coding! 😊

Made with contrib.rocks.

More questions?

Owner

Name: Aim
Login: aimhubio
Kind: organization
Location: Berkeley, California

Website: https://aimstack.io
Twitter: aimstackio
Repositories: 10
Profile: https://github.com/aimhubio

AI Development Environment

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you want to cite the tool, feel free to use this (but only if you loved it 😊)"
title: "Aim"
abstract: "An easy-to-use and performant open-source ML experiment tracking tool."
date-released: 2020-06-18
authors:
  - family-names: "Arakelyan"
    given-names: "Gor"
  - family-names: "Soghomonyan"
    given-names: "Gevorg"
  - name: "The Aim team"
version: 3.9.3
doi: 10.5281/zenodo.6536395
license: "Apache-2.0"
url: "https://aimstack.io"
repository-code: "https://github.com/aimhubio/aim"

Committers

Last synced: 7 months ago

All Time

Total Commits: 1,965
Total Committers: 82
Avg Commits per committer: 23.963
Development Distribution Score (DDS): 0.745

Past Year

Commits: 85
Committers: 23
Avg Commits per committer: 3.696
Development Distribution Score (DDS): 0.588

Top Committers

Name	Email	Commits
Gor Arakelyan	a**0@g**m	501
mihran113	v**n@g**m	230
alberttorosyan	3****n	223
Arsen Alkamyan	4****t	204
Vardges Hovhannisyan	4****v	160
Karo	4****d	151
Ruben Karapetyan	k**n@g**m	128
Ruben Aprikyan	r**n@g**m	79
SGevorg	g**s@g**m	65
Arthur	4****e	54
Karen Hambardzumyan	m**k@g**m	24
Hovhannes Tamoyan	h**n@g**m	23
Hamik Hambardzumyan	h**5@g**m	13
aramaim	a**m@a**o	6
Mikael Manukyan	a**t@g**m	6
dependabot[bot]	4****]	5
Sidney Batchelder	4****r	4
Sharath MK	S**9@g**m	3
Marten Lienen	m**n@g**m	3
Khazhak Galstyan	3****k	3
Kento Nozawa	k**w@k**p	3
Jialin Wu	4****2	3
Alan Saul	a****l	3
Jan Philip Göpfert	g**b@2**u	2
fetz236	5****6	2
bhaveshk658	5****8	2
Xingkai Yu	3****r	2
Vassilis Vassiliadis	4****s	2
Tatevv	t**n@g**m	2
Nicholas Gao	n**6@g**m	2
and 52 more...

Committer Domains (Top 20 + Academic)

anyscale.com: 1 tagtraum.com: 1 163.com: 1 motius.de: 1 chandergovind.org: 1 qq.com: 1 imada.sdu.dk: 1 uduse.com: 1 zoho.com: 1 mkraus.io: 1 mail.ru: 1 berkeley.edu: 1 2cute.eu: 1 klis.tsukuba.ac.jp: 1 aimhub.io: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 253
Total pull requests: 500
Average time to close issues: 4 months
Average time to close pull requests: 24 days
Total issue authors: 192
Total pull request authors: 72
Average comments per issue: 2.56
Average comments per pull request: 0.99
Merged pull requests: 317
Bot issues: 0
Bot pull requests: 73

Past Year

Issues: 54
Pull requests: 127
Average time to close issues: 14 days
Average time to close pull requests: 11 days
Issue authors: 41
Pull request authors: 19
Average comments per issue: 0.65
Average comments per pull request: 0.94
Merged pull requests: 66
Bot issues: 0
Bot pull requests: 38

View more stats

Top Authors

Issue Authors

jennifer12121 (6)
SGevorg (5)
mihran113 (4)
sbatchelder (4)
alberttorosyan (4)
zhiyxu (3)
tmynn (3)
Nuno-Mota (3)
bryanwhiting (3)
alansaul (3)
YodaEmbedding (3)
thuzhf (3)
bluenote10 (3)
Engrammae (3)
Sharathmk99 (3)

Pull Request Authors

mihran113 (94)
dependabot[bot] (88)
alberttorosyan (79)
roubkar (43)
KaroMourad (41)
arsengit (37)
sbatchelder (8)
SGevorg (8)
tmynn (8)
mauricekraus (6)
gorarakelyan (6)
ibarrionuevo (6)
martenlienen (6)
VassilisVassiliadis (4)
bluenote10 (4)

Top Labels

Issue Labels

type / bug (114) help wanted (111) type / enhancement (77) type / question (50) area / Web-UI (28) area / SDK-storage (27) phase / ready-to-go (12) phase / shipped (9) type / code-health (7) phase / exploring (6) area / integrations (6) phase / in-progress (4) priority / critical-urgent (3) area / docs (3) good first issue (2) type / poor-performance (1) phase / review-needed (1) area / onboarding (1)

Pull Request Labels

dependencies (88) javascript (73) python (15) area / Web-UI (4) phase / on-hold (3) area / packaging (2) type / enhancement (1)

Packages

Total packages: 3
Total downloads:
- pypi 140,853 last-month
Total docker downloads: 24,426,752

Total dependent packages: 18
(may contain duplicates)
Total dependent repositories: 136
(may contain duplicates)
Total versions: 1,198
Total maintainers: 3
Total advisories: 20

pypi.org: aim

A super-easy way to record, search and compare AI experiments.

Documentation: https://aim.readthedocs.io/
License: Apache Software License
Latest release: 4.0.3
published about 2 years ago

Versions: 1,018
Dependent Packages: 18
Dependent Repositories: 136
Downloads: 140,853 Last month
Docker Downloads: 24,426,752

Rankings

Docker downloads count: 0.7%

Downloads: 0.9%

Average: 1.3%

Dependent repos count: 1.3%

Dependent packages count: 2.2%

Maintainers (3)

albert1990 sgevorg mvanyan

Advisories (20)

Last synced: 12 months ago

proxy.golang.org: github.com/aimhubio/aim

Documentation: https://pkg.go.dev/github.com/aimhubio/aim#section-documentation
License: apache-2.0
Latest release: v4.0.3+incompatible
published about 2 years ago

Versions: 177
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 7.0%

Average: 8.2%

Dependent repos count: 9.3%

Last synced: 4 months ago

conda-forge.org: aim

Homepage: https://github.com/aimhubio/aim
License: Apache-2.0
Latest release: 2.7.4
published about 4 years ago

Versions: 3
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Stargazers count: 6.8%

Forks count: 12.3%

Average: 26.1%

Dependent repos count: 34.0%

Dependent packages count: 51.2%

Last synced: 4 months ago

Dependencies

docs/requirements.txt pypi

Cython ==3.0.0a9
m2r2 ==0.3.1
mistune ==0.8.4
sphinx *
sphinx-copybutton *
sphinx_rtd_theme *

.github/workflows/create-docker-image.yml actions

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.github/workflows/python-package.yml actions

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2746 dependencies

aim/web/ui/package.json npm

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@material-ui/icons ^4.11.2
@material-ui/lab ^4.0.0-alpha.60
@monaco-editor/react 4.4.4
@radix-ui/primitive ^1.0.0
@radix-ui/react-checkbox ^1.0.1
@radix-ui/react-dialog ^1.0.2
@radix-ui/react-popover ^1.0.2
@radix-ui/react-radio-group ^1.1.0
@radix-ui/react-separator ^1.0.1
@radix-ui/react-slider ^1.1.0
@radix-ui/react-slot ^1.0.1
@radix-ui/react-switch ^1.0.1
@radix-ui/react-toast ^1.1.2
@radix-ui/react-tooltip ^1.0.3
@stitches/react ^1.2.8
@storybook/react ^6.5.12
@tabler/icons-react ^2.2.0
@uiw/react-textarea-code-editor ^1.4.14
antd ^4.23.6
bs58check ^2.1.2
classnames ^2.3.1
comlink ^4.3.1
d3 ^7.0.0
dot-object ^2.1.4
file-saver ^2.0.5
formik ^2.2.9
highcharts ^9.3.1
highcharts-react-official ^3.1.0
history ^5.3.0
humanize-duration ^3.27.0
immer ^9.0.16
lodash-es ^4.17.21
marked ^4.1.1
material-ui-audio-player ^1.7.1
md5 ^2.3.0
memoize-one ^5.2.1
moment ^2.29.4
monaco-editor ^0.33.0
plotly.js ^2.7.0
prop-types ^15.7.2
prosemirror-tables ^1.1.1
react ^17.0.2
react-accessible-dropdown-menu-hook ^3.1.0
react-beautiful-dnd ^13.1.0
react-dom ^17.0.2
react-plotly.js ^2.5.1
react-router-dom ^5.3.0
react-scripts 4.0.3
react-select ^5.5.4
react-split ^2.0.14
react-virtualized ^9.22.3
react-virtualized-auto-sizer ^1.0.5
react-virtualized-tree ^3.4.1
react-window ^1.8.6
rich-markdown-editor ^11.21.3
styled-components ^5.3.3
typeface-roboto ^1.1.13
web-vitals ^1.0.1
yup ^0.32.9
zustand ^4.1.1

package-lock.json npm

aim/web/ui/setup.py pypi

examples/requirements.txt pypi

Keras ==2.2.4
Keras-Applications ==1.0.8
Keras-Preprocessing ==1.1.0
Pillow >=7.1.0
aim >=3.0.0
numpy ==1.21.0
scikit-learn ==0.21.2
scipy ==1.3.0
tensorboard ==1.13.1
tensorflow ==2.5.3
tensorflow-estimator ==1.13.0
termcolor ==1.1.0
torch ==1.2.0
torchvision ==0.4.0
wandb ==0.13.1

performance_tests/requirements.txt pypi

boto3 * test
parameterized ==0.8.1 test
pytest * test

pyproject.toml pypi

requirements.dev.txt pypi

twine >=1.11.0 development
wheel >=0.31.0 development

requirements.txt pypi

setup.py pypi

tests/requirements.txt pypi

deeplake * test
fastapi >=0.87.0 test
flake8 * test
httpx * test
pandas * test
parameterized ==0.8.1 test
pytest * test
pytest-cov ==2.12.1 test
tensorflow * test
torch * test