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Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production. <img src="./assets/logo-dark.svg#gh-dark-mode-only" width="600" alt="Weights & Biases" /> <img src="./assets/logo-light.svg#gh-light-mode-only" width="600" alt="Weights & Biases" /> <a href="https://pypi.python.org/pypi/wandb"><img src="https://img.shields.io/pypi/v/wandb" /></a> <a href="https://anaconda.org/conda-forge/wandb"><img src="https://img.shields.io/conda/vn/conda-forge/wandb" /></a> <a href="https://pypi.python.org/pypi/wandb"><img src="https://img.shields.io/pypi/pyversions/wandb" /></a> <a href="https://circleci.com/gh/wandb/wandb"><img src="https://img.shields.io/circleci/build/github/wandb/wandb/main" /></a> <a href="https://codecov.io/gh/wandb/wandb"><img src="https://img.shields.io/codecov/c/gh/wandb/wandb" /></a> <a href="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Intro_to_Weights_%26_Biases.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" /></a> Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production machine learning models. Get started with W&B today, [sign up for a W&B account!](https://wandb.com?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme) Building an LLM app? Track, debug, evaluate, and monitor LLM apps with [Weave](https://wandb.github.io/weave?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme), our new suite of tools for GenAI.   # Documentation <a target="_blank" href="https://docs.wandb.ai/guides/track?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme"> <picture> <source media="(prefers-color-scheme: dark)" srcset="./assets/Product_Icons_dark_background/experiments-dark.svg" width="12.5%"> <source media="(prefers-color-scheme: light)" srcset="./assets/Product_Icons_light/experiments-light.svg" width="12.5%"> <img alt="Weights and Biases Experiments" src=""> </picture> </a> <a target="_blank" href="https://docs.wandb.ai/guides/reports?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme"> <picture> <source media="(prefers-color-scheme: dark)" srcset="./assets/Product_Icons_dark_background/reports-dark.svg" width="12.5%"> <source media="(prefers-color-scheme: light)" srcset="./assets/Product_Icons_light/reports-light.svg" width="12.5%"> <img alt="Weights and Biases Reports" src=""> </picture> </a> <a target="_blank" href="https://docs.wandb.ai/guides/artifacts?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme"> <picture> <source media="(prefers-color-scheme: dark)" srcset="./assets/Product_Icons_dark_background/artifacts-dark.svg" width="12.5%"> <source media="(prefers-color-scheme: light)" srcset="./assets/Product_Icons_light/artifacts-light.svg" width="12.5%"> <img alt="Weights and Biases Artifacts" src=""> </picture> </a> <a target="_blank" href="https://docs.wandb.ai/guides/tables?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme"> <picture> <source media="(prefers-color-scheme: dark)" srcset="./assets/Product_Icons_dark_background/tables-dark.svg" width="12.5%"> <source media="(prefers-color-scheme: light)" srcset="./assets/Product_Icons_light/tables-light.svg" width="12.5%"> <img alt="Weights and Biases Tables" src=""> </picture> </a> <a target="_blank" href="https://docs.wandb.ai/guides/sweeps?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme"> <picture> <source media="(prefers-color-scheme: dark)" srcset="./assets/Product_Icons_dark_background/sweeps-dark.svg" width="12.5%"> <source media="(prefers-color-scheme: light)" srcset="./assets/Product_Icons_light/sweeps-light.svg" width="12.5%"> <img alt="Weights and Biases Sweeps" src=""> </picture> </a> <a target="_blank" href="https://docs.wandb.ai/guides/model_registry?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme"> <picture> <source media="(prefers-color-scheme: dark)" srcset="./assets/Product_Icons_dark_background/model-registry-dark.svg" width="12.5%"> <source media="(prefers-color-scheme: light)" srcset="./assets/Product_Icons_light/model-registry-light.svg" width="12.5%"> <img alt="Weights and Biases Model Management" src=""> </picture> </a> <a target="_blank" href="https://docs.wandb.ai/guides/artifacts/project-scoped-automations?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme"> <picture> <source media="(prefers-color-scheme: dark)" srcset="./assets/Product_Icons_dark_background/automations-dark.svg" width="12.5%"> <source media="(prefers-color-scheme: light)" srcset="./assets/Product_Icons_light/automations-light.svg" width="12.5%"> <img alt="Weights and Biases Prompts" src=""> </picture> See the [W&B Developer Guide](https://docs.wandb.ai/?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=documentation) and [API Reference Guide](https://docs.wandb.ai/ref?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=documentation) for a full technical description of the W&B platform. # Quickstart Get started with W&B in four steps: 1. First, sign up for a [W&B account](https://wandb.ai/login?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=quickstart). 2. Second, install the W&B SDK with [pip](https://pip.pypa.io/en/stable/). Navigate to your terminal and type the following command: ```bash pip install wandb ``` 3. Third, log into W&B: ```python wandb.login() ``` 4. Use the example code snippet below as a template to integrate W&B to your Python script: ```python import wandb # Start a W&B Run with wandb.init run = wandb.init(project="my_first_project") # Save model inputs and hyperparameters in a wandb.config object config = run.config config.learning_rate = 0.01 # Model training code here ... # Log metrics over time to visualize performance with wandb.log for i in range(10): run.log({"loss": ...}) # Mark the run as finished, and finish uploading all data run.finish() ``` That's it! Navigate to the W&B App to view a dashboard of your first W&B Experiment. Use the W&B App to compare multiple experiments in a unified place, dive into the results of a single run, and much more! <img src="./assets/wandb_demo_experiments.gif" width="100%"> Example W&B Dashboard that shows Runs from an Experiment.   # Integrations Use your favorite framework with W&B. W&B integrations make it fast and easy to set up experiment tracking and data versioning inside existing projects. For more information on how to integrate W&B with the framework of your choice, see the [Integrations chapter](https://docs.wandb.ai/guides/integrations) in the W&B Developer Guide.  <details> <summary>🔥 PyTorch</summary> Call `.watch` and pass in your PyTorch model to automatically log gradients and store the network topology. Next, use `.log` to track other metrics. The following example demonstrates an example of how to do this: ```python import wandb # 1. Start a new run run = wandb.init(project="gpt4") # 2. Save model inputs and hyperparameters config = run.config config.dropout = 0.01 # 3. Log gradients and model parameters run.watch(model) for batch_idx, (data, target) in enumerate(train_loader): ... if batch_idx % args.log_interval == 0: # 4. Log metrics to visualize performance run.log({"loss": loss}) ``` - Run an example [Google Colab Notebook](http://wandb.me/pytorch-colab). - Read the [Developer Guide](https://docs.wandb.com/guides/integrations/pytorch?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations) for technical details on how to integrate PyTorch with W&B. - Explore [W&B Reports](https://app.wandb.ai/wandb/getting-started/reports/Pytorch--VmlldzoyMTEwNzM?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations). </details> <details> <summary>🌊 TensorFlow/Keras</summary> Use W&B Callbacks to automatically save metrics to W&B when you call `model.fit` during training. The following code example demonstrates how your script might look like when you integrate W&B with Keras: ```python # This script needs these libraries to be installed: # tensorflow, numpy import wandb from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint import random import numpy as np import tensorflow as tf # Start a run, tracking hyperparameters run = wandb.init( # set the wandb project where this run will be logged project="my-awesome-project", # track hyperparameters and run metadata with wandb.config config={ "layer_1": 512, "activation_1": "relu", "dropout": random.uniform(0.01, 0.80), "layer_2": 10, "activation_2": "softmax", "optimizer": "sgd", "loss": "sparse_categorical_crossentropy", "metric": "accuracy", "epoch": 8, "batch_size": 256, }, ) # [optional] use wandb.config as your config config = run.config # get the data mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 x_train, y_train = x_train[::5], y_train[::5] x_test, y_test = x_test[::20], y_test[::20] labels = [str(digit) for digit in range(np.max(y_train) + 1)] # build a model model = tf.keras.models.Sequential( [ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(config.layer_1, activation=config.activation_1), tf.keras.layers.Dropout(config.dropout), tf.keras.layers.Dense(config.layer_2, activation=config.activation_2), ] ) # compile the model model.compile(optimizer=config.optimizer, loss=config.loss, metrics=[config.metric]) # WandbMetricsLogger will log train and validation metrics to wandb # WandbModelCheckpoint will upload model checkpoints to wandb history = model.fit( x=x_train, y=y_train, epochs=config.epoch, batch_size=config.batch_size, validation_data=(x_test, y_test), callbacks=[ WandbMetricsLogger(log_freq=5), WandbModelCheckpoint("models"), ], ) # [optional] finish the wandb run, necessary in notebooks run.finish() ``` Get started integrating your Keras model with W&B today: - Run an example [Google Colab Notebook](https://wandb.me/intro-keras?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations) - Read the [Developer Guide](https://docs.wandb.com/guides/integrations/keras?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations) for technical details on how to integrate Keras with W&B. - Explore [W&B Reports](https://app.wandb.ai/wandb/getting-started/reports/Keras--VmlldzoyMTEwNjQ?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations). </details> <details> <summary>🤗 Hugging Face Transformers</summary> Pass `wandb` to the `report_to` argument when you run a script using a Hugging Face Trainer. W&B will automatically log losses, evaluation metrics, model topology, and gradients. **Note**: The environment you run your script in must have `wandb` installed. The following example demonstrates how to integrate W&B with Hugging Face: ```python # This script needs these libraries to be installed: # numpy, transformers, datasets import wandb import os import numpy as np from datasets import load_dataset from transformers import TrainingArguments, Trainer from transformers import AutoTokenizer, AutoModelForSequenceClassification def tokenize_function(examples): return tokenizer(examples["text"], padding="max_length", truncation=True) def compute_metrics(eval_pred): logits, labels = eval_pred predictions = np.argmax(logits, axis=-1) return {"accuracy": np.mean(predictions == labels)} # download prepare the data dataset = load_dataset("yelp_review_full") tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") small_train_dataset = dataset["train"].shuffle(seed=42).select(range(1000)) small_eval_dataset = dataset["test"].shuffle(seed=42).select(range(300)) small_train_dataset = small_train_dataset.map(tokenize_function, batched=True) small_eval_dataset = small_train_dataset.map(tokenize_function, batched=True) # download the model model = AutoModelForSequenceClassification.from_pretrained( "distilbert-base-uncased", num_labels=5 ) # set the wandb project where this run will be logged os.environ["WANDB_PROJECT"] = "my-awesome-project" # save your trained model checkpoint to wandb os.environ["WANDB_LOG_MODEL"] = "true" # turn off watch to log faster os.environ["WANDB_WATCH"] = "false" # pass "wandb" to the `report_to` parameter to turn on wandb logging training_args = TrainingArguments( output_dir="models", report_to="wandb", logging_steps=5, per_device_train_batch_size=32, per_device_eval_batch_size=32, evaluation_strategy="steps", eval_steps=20, max_steps=100, save_steps=100, ) # define the trainer and start training trainer = Trainer( model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset, compute_metrics=compute_metrics, ) trainer.train() # [optional] finish the wandb run, necessary in notebooks wandb.finish() ``` - Run an example [Google Colab Notebook](http://wandb.me/hf?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations). - Read the [Developer Guide](https://docs.wandb.com/guides/integrations/huggingface?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations) for technical details on how to integrate Hugging Face with W&B. </details> <details> <summary>⚡️ PyTorch Lightning</summary> Build scalable, structured, high-performance PyTorch models with Lightning and log them with W&B. ```python # This script needs these libraries to be installed: # torch, torchvision, pytorch_lightning import wandb import os from torch import optim, nn, utils from torchvision.datasets import MNIST from torchvision.transforms import ToTensor import pytorch_lightning as pl from pytorch_lightning.loggers import WandbLogger class LitAutoEncoder(pl.LightningModule): def __init__(self, lr=1e-3, inp_size=28, optimizer="Adam"): super().__init__() self.encoder = nn.Sequential( nn.Linear(inp_size * inp_size, 64), nn.ReLU(), nn.Linear(64, 3) ) self.decoder = nn.Sequential( nn.Linear(3, 64), nn.ReLU(), nn.Linear(64, inp_size * inp_size) ) self.lr = lr # save hyperparameters to self.hparamsm auto-logged by wandb self.save_hyperparameters() def training_step(self, batch, batch_idx): x, y = batch x = x.view(x.size(0), -1) z = self.encoder(x) x_hat = self.decoder(z) loss = nn.functional.mse_loss(x_hat, x) # log metrics to wandb self.log("train_loss", loss) return loss def configure_optimizers(self): optimizer = optim.Adam(self.parameters(), lr=self.lr) return optimizer # init the autoencoder autoencoder = LitAutoEncoder(lr=1e-3, inp_size=28) # setup data batch_size = 32 dataset = MNIST(os.getcwd(), download=True, transform=ToTensor()) train_loader = utils.data.DataLoader(dataset, shuffle=True) # initialise the wandb logger and name your wandb project wandb_logger = WandbLogger(project="my-awesome-project") # add your batch size to the wandb config wandb_logger.experiment.config["batch_size"] = batch_size # pass wandb_logger to the Trainer trainer = pl.Trainer(limit_train_batches=750, max_epochs=5, logger=wandb_logger) # train the model trainer.fit(model=autoencoder, train_dataloaders=train_loader) # [optional] finish the wandb run, necessary in notebooks wandb.finish() ``` - Run an example [Google Colab Notebook](http://wandb.me/lightning?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations). - Read the [Developer Guide](https://docs.wandb.ai/guides/integrations/lightning?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations) for technical details on how to integrate PyTorch Lightning with W&B. </details> <details> <summary>💨 XGBoost</summary> Use W&B Callbacks to automatically save metrics to W&B when you call `model.fit` during training. The following code example demonstrates how your script might look like when you integrate W&B with XGBoost: ```python # This script needs these libraries to be installed: # numpy, xgboost import wandb from wandb.xgboost import WandbCallback import numpy as np import xgboost as xgb # setup parameters for xgboost param = { "objective": "multi:softmax", "eta": 0.1, "max_depth": 6, "nthread": 4, "num_class": 6, } # start a new wandb run to track this script run = wandb.init( # set the wandb project where this run will be logged project="my-awesome-project", # track hyperparameters and run metadata config=param, ) # download data from wandb Artifacts and prep data run.use_artifact("wandb/intro/dermatology_data:v0", type="dataset").download(".") data = np.loadtxt( "./dermatology.data", delimiter=",", converters={33: lambda x: int(x == "?"), 34: lambda x: int(x) - 1}, ) sz = data.shape train = data[: int(sz[0] * 0.7), :] test = data[int(sz[0] * 0.7) :, :] train_X = train[:, :33] train_Y = train[:, 34] test_X = test[:, :33] test_Y = test[:, 34] xg_train = xgb.DMatrix(train_X, label=train_Y) xg_test = xgb.DMatrix(test_X, label=test_Y) watchlist = [(xg_train, "train"), (xg_test, "test")] # add another config to the wandb run num_round = 5 run.config["num_round"] = 5 run.config["data_shape"] = sz # pass WandbCallback to the booster to log its configs and metrics bst = xgb.train( param, xg_train, num_round, evals=watchlist, callbacks=[WandbCallback()] ) # get prediction pred = bst.predict(xg_test) error_rate = np.sum(pred != test_Y) / test_Y.shape[0] # log your test metric to wandb run.summary["Error Rate"] = error_rate # [optional] finish the wandb run, necessary in notebooks run.finish() ``` - Run an example [Google Colab Notebook](https://wandb.me/xgboost?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations). - Read the [Developer Guide](https://docs.wandb.ai/guides/integrations/xgboost?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations) for technical details on how to integrate XGBoost with W&B. </details> <details> <summary>🧮 Sci-Kit Learn</summary> Use wandb to visualize and compare your scikit-learn models' performance: ```python # This script needs these libraries to be installed: # numpy, sklearn import wandb from wandb.sklearn import plot_precision_recall, plot_feature_importances from wandb.sklearn import plot_class_proportions, plot_learning_curve, plot_roc import numpy as np from sklearn import datasets from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split # load and process data wbcd = datasets.load_breast_cancer() feature_names = wbcd.feature_names labels = wbcd.target_names test_size = 0.2 X_train, X_test, y_train, y_test = train_test_split( wbcd.data, wbcd.target, test_size=test_size ) # train model model = RandomForestClassifier() model.fit(X_train, y_train) model_params = model.get_params() # get predictions y_pred = model.predict(X_test) y_probas = model.predict_proba(X_test) importances = model.feature_importances_ indices = np.argsort(importances)[::-1] # start a new wandb run and add your model hyperparameters run = wandb.init(project="my-awesome-project", config=model_params) # Add additional configs to wandb run.config.update( { "test_size": test_size, "train_len": len(X_train), "test_len": len(X_test), } ) # log additional visualisations to wandb plot_class_proportions(y_train, y_test, labels) plot_learning_curve(model, X_train, y_train) plot_roc(y_test, y_probas, labels) plot_precision_recall(y_test, y_probas, labels) plot_feature_importances(model) # [optional] finish the wandb run, necessary in notebooks run.finish() ``` - Run an example [Google Colab Notebook](https://wandb.me/scikit-colab?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations). - Read the [Developer Guide](https://docs.wandb.ai/guides/integrations/scikit?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=integrations) for technical details on how to integrate Scikit-Learn with W&B. </details>   # W&B Hosting Options Weights & Biases is available in the cloud or installed on your private infrastructure. Set up a W&B Server in a production environment in one of three ways: 1. [Production Cloud](https://docs.wandb.ai/guides/hosting/hosting-options/self-managed#on-prem-private-cloud?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=hosting): Set up a production deployment on a private cloud in just a few steps using terraform scripts provided by W&B. 2. [Dedicated Cloud](https://docs.wandb.ai/guides/hosting/hosting-options/wb-managed#dedicated-cloud?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=hosting): A managed, dedicated deployment on W&B's single-tenant infrastructure in your choice of cloud region. 3. [On-Prem/Bare Metal](https://docs.wandb.ai/guides/hosting/how-to-guides/bare-metal?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=hosting): W&B supports setting up a production server on most bare metal servers in your on-premise data centers. Quickly get started by running `wandb server` to easily start hosting W&B on your local infrastructure. See the [Hosting documentation](https://docs.wandb.ai/guides/hosting?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=hosting) in the W&B Developer Guide for more information.    # Python Version Support We are committed to supporting our minimum required Python version for *at least* six months after its official end-of-life (EOL) date, as defined by the Python Software Foundation. You can find a list of Python EOL dates [here](https://devguide.python.org/versions/). When we discontinue support for a Python version, we will increment the library’s minor version number to reflect this change.   # Contribution guidelines Weights & Biases ❤️ open source, and we welcome contributions from the community! See the [Contribution guide](https://github.com/wandb/wandb/blob/main/CONTRIBUTING.md) for more information on the development workflow and the internals of the wandb library. For wandb bugs and feature requests, visit [GitHub Issues](https://github.com/wandb/wandb/issues) or contact [email protected].   # W&B Community Be a part of the growing W&B Community and interact with the W&B team in our [Discord](https://wandb.me/discord). Stay connected with the latest ML updates and tutorials with [W&B Fully Connected](https://wandb.ai/fully-connected).   # License [MIT License](https://github.com/wandb/wandb/blob/main/LICENSE) ", Assign "at most 3 tags" to the expected json: {"id":"10505","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"

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