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Top 23 Python Open-Source Projects
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system-design-primer
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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AutoGPT
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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HelloGitHub
:octocat: 分享 GitHub 上有趣、入门级的开源项目。Share interesting, entry-level open source projects on GitHub.
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Home Assistant
:house_with_garden: Open source home automation that puts local control and privacy first.
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devops-exercises
Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions
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Ansible
Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
✅ donnemartin/system-design-primer: https://github.com/donnemartin/system-design-primer
8. Vinta/Awesome-python - This repository is a curated list of top Python frameworks, libraries, and tools for a variety of purposes. It's a must-visit for any developer looking to expand their Python skills and discover new resources. https://github.com/vinta/awesome-python
# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")
3. TheAlgorithms/Python - For those interested in algorithms and data structures, this repository offers Python implementations for a wide range of algorithms. It's a great way to deepen understanding of algorithmic learning with Python. https://github.com/TheAlgorithms/Python
9. Practical-tutorials/project-based-learning - This repository provides links to project-based tutorials for various programming languages, with a focus on Python. It's a great way to gain practical experience and build your developer portfolio. https://github.com/practical-tutorials/project-based-learning
AutoGPT is a framework that seems nice. It has a cool CLI and a flutter UI to create agents from the browser. Its main purpose is to work with your local stuff (documents, audio, videos, etc)
Project mention: Show HN: I made an app to use local AI as daily driver | news.ycombinator.com | 2024-02-27* LLaVA model: I'll add more documentation. You are right Llava could not generate images. For image generation I don't have immediate plans, but checkout these projects for local image generation.
- https://diffusionbee.com/
- https://github.com/comfyanonymous/ComfyUI
- https://github.com/AUTOMATIC1111/stable-diffusion-webui
Project mention: Reading list to join AI field from Hugging Face cofounder | news.ycombinator.com | 2024-05-18Not sure what you are implying. Thomas Wolf has the second highest number of commits on HuggingFace/transformers. He is clearly competent & deeply technical
https://github.com/huggingface/transformers/
Project mention: 30-seconds-of-code: Short code snippets for all your development needs | news.ycombinator.com | 2023-12-25
Project mention: Control Linux based distros using hand gestures using OpenCV, GTK, Mediapipe | news.ycombinator.com | 2024-04-14Are you by chance interested in a command named after the four-letter word, which automatically fixes and reruns the last command: https://github.com/nvbn/thefuck
Project mention: PyTorch 2.3: User-Defined Triton Kernels, Tensor Parallelism in Distributed | news.ycombinator.com | 2024-05-10
Django has long been the most popular Python framework for developing web applications. One of its most powerful features is its built in object-relational mapper (ORM) which is designed to flexibly and safely interact with SQL databases in an abstract way.
Project mention: Python FastAPI: Integrating OAuth2 Security with the Application's Own Authentication Process | dev.to | 2024-05-13
I think the "or other url" is important here. I didn't realise for a long time that I could put a reddit, Twitter or other URLs in there to download videos. You can find a complete list of supported sites here:
https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites....
Project mention: Do not buy a Hisense TV (or at least keep them offline) | news.ycombinator.com | 2024-04-20Apparently the same issue has been reported with Philips TV [1] and Fritz!Box [2] as well.
[1] https://github.com/home-assistant/core/issues/73643#issuecom...
[2] https://forum.openwrt.org/t/minidlna-creates-new-media-serve...
- https://github.com/microsoft/ML-For-Beginners
Also check out this list Pitt puts out every year:
Project mention: Ask HN: High quality Python scripts or small libraries to learn from | news.ycombinator.com | 2024-04-19I'd suggest Flask or some of the smaller projects in the Pallets ecosystem:
https://github.com/pallets/flask
Ansible is an open-source IT automation tool that simplifies application deployment, cloud provisioning, and configuration management across diverse environments. It uses a declarative language to describe the desired state of the system, and then takes the necessary actions to achieve that state. Ansible has become incredibly popular due to its simplicity, agentless architecture, and extensive community support. Document: ansible.com, ansible basics
Project mention: Side Quest #3: maybe the real Deepfakes were the friends we made along the way | dev.to | 2024-05-20def batcher_from_directory(batch_size:int, dataset_path:str, shuffle=False,seed=None) -> tf.data.Dataset: """ Return a tensorflow Dataset object that returns images and spectrograms as required. Partly inspired by https://github.com/keras-team/keras/blob/v3.3.3/keras/src/utils/image_dataset_utils.py Args: batch_size: The batch size. dataset_path: The path to the dataset folder which must contain the image folder and audio folder. shuffle: Whether to shuffle the dataset. Default to False. seed: The seed for the shuffle. Default to None. """ image_dataset_path = os.path.join(dataset_path, "image") # create the foundation datasets og_dataset = tf.data.Dataset.from_generator(lambda: original_image_path_gen(image_dataset_path), output_signature=tf.TensorSpec(shape=(), dtype=tf.string)) og_dataset = og_dataset.repeat(None) # repeat indefinitely ref_dataset = tf.data.Dataset.from_generator(lambda: ref_image_path_gen(image_dataset_path), output_signature=(tf.TensorSpec(shape=(), dtype=tf.string), tf.TensorSpec(shape=(), dtype=tf.bool))) ref_dataset = ref_dataset.repeat(None) # repeat indefinitely # create the input datasets og_image_dataset = og_dataset.map(lambda x: tf.py_function(load_image, [x, tf.convert_to_tensor(False, dtype=tf.bool)], tf.float32), num_parallel_calls=tf.data.AUTOTUNE) masked_image_dataset = og_image_dataset.map(lambda x: tf.py_function(load_masked_image, [x], tf.float32), num_parallel_calls=tf.data.AUTOTUNE) ref_image_dataset = ref_dataset.map(lambda x, y: tf.py_function(load_image, [x, y], tf.float32), num_parallel_calls=tf.data.AUTOTUNE) audio_spec_dataset = og_dataset.map(lambda x: tf.py_function(load_audio_data, [x, dataset_path], tf.float64), num_parallel_calls=tf.data.AUTOTUNE) unsync_spec_dataset = ref_dataset.map(lambda x, _: tf.py_function(load_audio_data, [x, dataset_path], tf.float64), num_parallel_calls=tf.data.AUTOTUNE) # ensure shape as tensorflow does not accept unknown shapes og_image_dataset = og_image_dataset.map(lambda x: tf.ensure_shape(x, IMAGE_SHAPE)) masked_image_dataset = masked_image_dataset.map(lambda x: tf.ensure_shape(x, MASKED_IMAGE_SHAPE)) ref_image_dataset = ref_image_dataset.map(lambda x: tf.ensure_shape(x, IMAGE_SHAPE)) audio_spec_dataset = audio_spec_dataset.map(lambda x: tf.ensure_shape(x, AUDIO_SPECTROGRAM_SHAPE)) unsync_spec_dataset = unsync_spec_dataset.map(lambda x: tf.ensure_shape(x, AUDIO_SPECTROGRAM_SHAPE)) # multi input using https://discuss.tensorflow.org/t/train-a-model-on-multiple-input-dataset/17829/4 full_dataset = tf.data.Dataset.zip((masked_image_dataset, ref_image_dataset, audio_spec_dataset, unsync_spec_dataset), og_image_dataset) # if shuffle: # full_dataset = full_dataset.shuffle(buffer_size=batch_size * 8, seed=seed) # not sure why buffer size is such # batch full_dataset = full_dataset.batch(batch_size=batch_size) return full_dataset
Project mention: Show HN: Open-source BI and analytics for engineers | news.ycombinator.com | 2024-05-15We are looking at moving our Power BI stuff to Apache Superset [1]. How does this compare to Superset?
[1] https://superset.apache.org/
Project mention: How to Build a Logistic Regression Model: A Spam-filter Tutorial | dev.to | 2024-05-05Online Courses: Coursera: "Machine Learning" by Andrew Ng edX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By understanding the core concepts of logistic regression, its limitations, and exploring further resources, you'll be well-equipped to navigate the exciting world of machine learning!
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
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A note from our sponsor - InfluxDB
www.influxdata.com | 20 May 2024
Index
What are some of the best open-source Python projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | system-design-primer | 257,707 |
| 2 | awesome-python | 207,283 |
| 3 | tensorflow | 182,857 |
| 4 | TheAlgorithms | 180,485 |
| 5 | project-based-learning | 171,454 |
| 6 | AutoGPT | 162,161 |
| 7 | stable-diffusion-webui | 131,121 |
| 8 | transformers | 126,170 |
| 9 | 30-seconds-of-code | 119,530 |
| 10 | HelloGitHub | 85,663 |
| 11 | thefuck | 83,068 |
| 12 | Pytorch | 78,436 |
| 13 | Django | 77,104 |
| 14 | fastapi | 71,659 |
| 15 | yt-dlp | 72,048 |
| 16 | Home Assistant | 69,033 |
| 17 | ML-For-Beginners | 67,267 |
| 18 | Flask | 66,538 |
| 19 | devops-exercises | 63,930 |
| 20 | Ansible | 61,353 |
| 21 | Keras | 61,044 |
| 22 | superset | 59,473 |
| 23 | scikit-learn | 58,344 |
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