PyPI 官网下载 | aim-3.0.0b6.dev1.tar.gz

<div align="center"> <img src="https://user-images.githubusercontent.com/13848158/97081166-8f568800-1611-11eb-991c-e9bc1344074e.png" height="95" /> **A super-easy way to record, search and compare 1000s of ML training runs** [](https://pypi.org/project/aim-cli/) [](https://pypi.org/project/aim-cli/) [](https://hub.docker.com/r/aimhubio/aim-board) [](http://github.com/aimhubio/aim/issues) [](https://opensource.org/licenses/Apache-2.0) [](https://twitter.com/aimstackio) --- <h3 align="center"> :tada: Try out Aim at <a href="http://play.aimstack.io:43900/explore?search=eyJjaGFydCI6eyJzZXR0aW5ncyI6eyJwZXJzaXN0ZW50Ijp7InlTY2FsZSI6MCwiem9vbU1vZGUiOmZhbHNlLCJ6b29tSGlzdG9yeSI6W10sInBlcnNpc3RlbnQiOnsieVNjYWxlIjowLCJ6b29tTW9kZSI6ZmFsc2UsInpvb21IaXN0b3J5IjpbXSwicGVyc2lzdGVudCI6eyJ5U2NhbGUiOjAsInpvb21Nb2RlIjpmYWxzZSwiem9vbUhpc3RvcnkiOltdLCJwZXJzaXN0ZW50Ijp7ImRpc3BsYXlPdXRsaWVycyI6ZmFsc2UsInpvb20iOm51bGwsImludGVycG9sYXRlIjpmYWxzZX0sImRpc3BsYXlPdXRsaWVycyI6ZmFsc2UsImludGVycG9sYXRlIjp0cnVlLCJ6b29tIjp7IjAiOnsieCI6WzIuNjg1MTczMTQ4MTA1MzM4NSwzOF0sInkiOlszLjQ1NDEwMDA0ODU0MjAyMywzLjY4MjMzOTM5NzAxNzAyOF19LCIxIjp7IngiOlsyLjUzNzIwNjUxMzA3NzU1NywzOF0sInkiOlsyNy42NzE5Nzg3NjM2MTk2ODQsMzIuNTgxOTk5Nzc4NzQ3NTZdfSwiMiI6eyJ4IjpbNi4xMzAwNzcxODU4MDYzMzIsMjddLCJ5IjpbMy40MzIxNTAwNTM5Nzc5NjYsMy42NDc3MDkzNTA2NzM4NjZdfSwiMyI6eyJ4IjpbMy40OTA5MDg3NzA3OTQyNDE0LDI3XSwieSI6WzI5LjE0MjQ0OTMzOTY5ODA4MiwzMy43NzI5OTk5NTQyMjM2MzZdfSwiNCI6eyJ4IjpbNC42OTMxNjM4MzkzNzM3MDgsMjhdLCJ5IjpbMjkuNDA5MjQ3MjgyOTAwMjY0LDMyLjEwMzk5OTcxMDA4MzAxXX0sIjUiOnsieCI6WzYuOTQxMTk3MDgwOTUyMjg1LDI3LjA0MTk0NTU0NjY2ODI2XSwieSI6WzMuNDgxNjAwMDgxOTIwNjI0LDMuNTgyNzgzODQ4MDA3ODUzOF19fX0sInpvb20iOm51bGx9LCJ6b29tIjpudWxsLCJkaXNwbGF5T3V0bGllcnMiOmZhbHNlLCJpbnRlcnBvbGF0ZSI6dHJ1ZSwiYWdncmVnYXRlZCI6dHJ1ZX19LCJmb2N1c2VkIjp7ImNpcmNsZSI6eyJhY3RpdmUiOmZhbHNlLCJydW5IYXNoIjpudWxsLCJtZXRyaWNOYW1lIjpudWxsLCJ0cmFjZUNvbnRleHQiOm51bGwsInN0ZXAiOm51bGx9fX0sInNlYXJjaCI6eyJxdWVyeSI6ImJsZXUgaWYgY29udGV4dC5zdWJzZXQgPT0gdGVzdCBhbmQgaHBhcmFtcy5sZWFybmluZ19yYXRlID4gMC4wMDAwMSIsInYiOjF9LCJjb250ZXh0RmlsdGVyIjp7Imdyb3VwQnlDb2xvciI6WyJwYXJhbXMuaHBhcmFtcy5hbGlnbiIsInBhcmFtcy5ocGFyYW1zLm1heF9rIiwicGFyYW1zLmRhdGFzZXQucHJlcHJvYyJdLCJncm91cEJ5U3R5bGUiOltdLCJncm91cEJ5Q2hhcnQiOlsicGFyYW1zLmhwYXJhbXMuYWxpZ24iXSwiZ3JvdXBBZ2FpbnN0Ijp7ImNvbG9yIjpmYWxzZSwic3R5bGUiOmZhbHNlLCJjaGFydCI6ZmFsc2V9LCJhZ2dyZWdhdGVkQXJlYSI6Im1pbl9tYXgiLCJhZ2dyZWdhdGVkTGluZSI6ImF2ZyIsInNlZWQiOnsiY29sb3IiOjEwLCJzdHlsZSI6MTB9LCJwZXJzaXN0Ijp7ImNvbG9yIjpmYWxzZSwic3R5bGUiOmZhbHNlfSwiYWdncmVnYXRlZCI6dHJ1ZX19">play.aimstack.io</a> :tada: <br /> <br /> Watch the tutorial <a href="https://youtu.be/s1dLlqik3j0">video</a> <br /> <br /> Join the Aim community on <a href="https://slack.aimstack.io">Slack</a> </h3> <br /> <img src="https://user-images.githubusercontent.com/13848158/104327872-addb6980-5504-11eb-8a5c-2d074a9ce735.gif" /> <br /> <h6 style="color: grey">Integrate seamlessly with your favorite tools</h6> <img src="https://user-images.githubusercontent.com/13848158/96861310-f7239c00-1474-11eb-82a4-4fa6eb2c6bb1.jpg" width="100" /> <img src="https://user-images.githubusercontent.com/13848158/96859323-6ba90b80-1472-11eb-9a6e-c60a90f11396.jpg" width="100" /> <img src="https://user-images.githubusercontent.com/13848158/96861315-f854c900-1474-11eb-8e9d-c7a07cda8445.jpg" width="100" /> <br /> <img src="https://user-images.githubusercontent.com/13848158/97086626-8b3c6180-1635-11eb-9e90-f215b898e298.png" width="100" /> <img src="https://user-images.githubusercontent.com/13848158/112145238-8cc58200-8bf3-11eb-8d22-bbdb8809f2aa.png" width="100" /> <img src="https://user-images.githubusercontent.com/13848158/118172152-17c93880-b43d-11eb-9169-785e4b52d89c.png" width="100" /> </div> Aim is an open-source comparison tool for AI experiments. With more resources and complex models more experiments are ran than ever. Use Aim to deeply inspect thousands of hyperparameter-sensitive training runs at once. # Getting Started in 3 Steps Follow the steps below to get started with Aim. **1. Install Aim on your training environment** _Prerequisite: You need to have python3 and pip3 installed in your environment before installing Aim_ ```shell $ pip install aim ``` **2. Integrate Aim with your code** <details open> <summary> Integrate your Python script </summary> ```python import aim # Save inputs, hparams or any other `key: value` pairs aim.set_params(hyperparam_dict, name='hparams') # Passing name argument is optional # ... for step in range(10): # Log metrics to visualize performance aim.track(metric_value, name='metric_name', epoch=epoch_number) # ... ``` _See documentation [here](#sdk-specifications)._ </details> <details> <summary> Integrate PyTorch Lightning </summary> ```python from aim.pytorch_lightning import AimLogger # ... trainer = pl.Trainer(logger=AimLogger(experiment='experiment_name')) # ... ``` _See documentation [here](#pytorch-lightning)._ </details> <details> <summary> Integrate Hugging Face </summary> ```python from aim.hugging_face import AimCallback # ... aim_callback = AimCallback(repo='/path/to/logs/dir', experiment='mnli') trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, callbacks=[aim_callback], # ... ) # ... ``` _See documentation [here](#hugging-face)._ </details> <details> <summary> Integrate Keras & tf.keras </summary> ```python import aim # ... model.fit(x_train, y_train, epochs=epochs, callbacks=[ aim.keras.AimCallback(repo='/path/to/logs/dir', experiment='experiment_name') # Use aim.tensorflow.AimCallback in case of tf.keras aim.tensorflow.AimCallback(repo='/path/to/logs/dir', experiment='experiment_name') ]) # ... ``` _See documentation [here](#tensorflow-and-keras)._ </details> <details> <summary> Integrate XGBoost </summary> ```python from aim.xgboost import AimCallback # ... aim_callback = AimCallback(repo='/path/to/logs/dir', experiment='experiment_name') bst = xgb.train(param, xg_train, num_round, watchlist, callbacks=[aim_callback]) # ... ``` _See documentation [here](#xgboost)._ </details> **3. Run the training as usual and start Aim UI** ```shell $ aim up ``` Jump to [[Overview](#overview)] [[SDK Specifications](#sdk-specifications)] [[Use Cases](#use-cases)] # Overview Aim helps you to compare 1000s of training runs at once through its framework-agnostic python SDK and performant UI. While using Aim SDK you create a Session object. It handles the tracking of metrics and parameters. When the training code is instrumented with Aim SDK's [Python Library](#sdk-specifications) and ran, Aim creates the `.aim` repository in your specified path and stores the data. Otherwise the data is created and stored in working directory. Additionally, Aim SDK also gives you flexibility to: - use multiple sessions in one training script to store multiple runs at once. When not initialized explicitly, Aim creates a default session. - use experiments to group related runs together. An experiment named `default` is created otherwise. - use integrations to automate tracking Jump to [[Getting Started](#getting-started-in-3-steps)] [[SDK Specifications](#sdk-specifications)] [[Use Cases](#use-cases)] # Democratizing AI Dev tools ## The mission... Aim's mission is to democratize AI dev tools. We believe that the best AI tools need to be: - open-source, open-data-format, community-driven, extensible - have great UI/UX, CLI and other interfaces for automatio