Jupyter Notebook VS Code • Install the Python extension. • ???????????? Install the Remote Development extension. • Python files can be run like Jupyter notebooks by delimiting cells/sections with

模型 训练 参数 调优 模型 部署 识别 服务 使用 Flask 快速搭建 验证码识别服务 使用 Flask 启动 验证码识别服务 $ export FLASK_ENV=development && flask run --host=0.0.0.0 打开浏览器访问测试 URL（http://localhost:5000/ping） 访问 验证码识别服务 $ curl -X POST

once again, so that the TPU doesn't complain about the # weights of the TF Hub models being on local storage. os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'UNCOMPRESSED' We first start by importing the multiple local minima might exist. Typical deep learning objective functions are non-convex too, and directly working with these functions might lead to the optimizer getting stuck in a local minima. Continuation add the harder examples we start to move towards the original loss landscape which might have many local minima. The authors see an improvement in model quality when using curriculum learning to train the

-it --rm -v local_dir:container_dir nvcr.io/nvidia/ pytorch:-py3 ‣ If you have Docker 19.02 or earlier, a typical command to launch the container is: nvidia-docker run -it --rm -v local_dir:container_dir --rm -v local_dir:container_dir nvcr.io/nvidia/ pytorch:-py3 ‣ If you have Docker 19.02 or earlier, a typical command to launch the container is: nvidia-docker run -it --rm -v local_dir:container_dir of PyTorch in /opt/ pytorch. It is prebuilt and installed in the default Python environment (/usr/local/lib/ python3.10/dist-packages/torch) in the container image. The container also includes the following:

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edu/~fritz/absps/waibelTDNN.pdf Moving window ▪ Inspired LeCun Recurrent Neural Network ▪ Spatial Local ▪ Temporal Local http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf Yoshua Bengio:1993

the K-means objective function L(µ, X, Z) = ∥X − Zµ∥2 It is a non-convex objective problem Many local minima possible Also NP-hard to minimize in general (note that Z is discrete) The K-means algorithm Fix Z, minimize w.r.t. µ (recompute the center means) Note: The algorithm usually converges to a local minima (though may not always, and it may just convergence “somewhere”). Multiple runs with different

Model register Status set/get Model delete Model Save Model Load HA Fault tolerance checkpoint Local HDFS Param Server System Model Serving System 3 在线机器学习-参数服务器 • 参数规模 • 支持百亿特征维度，千亿参数 • 模型版本 模型结构训练与推理兼容：在线PS与离线PS模型结构兼容，自动模型参数转换 • 稳定性优化 • 模型快照：基于ps-scheduler的周期模型版本探测与保存，模型稀疏化分片存储 • 冷备容灾：基于checkpoint机制(Local模式&Remote模式)，实现参数服务的高可用，支持基于模型的异构集群迁移，支持集 群扩缩容 • 性能优化 • 通信优化：数据请求(PULL&PUSH)聚合，同模型多矩阵并发，锁粒度优化，性能提升5-10倍

begin by understanding the model evaluation. Model Evaluation Evaluation is a process during development of the model to check whether the model is best fit for the given problem and corresponding data